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Effective Methods for Training Prompt Engineers: A Comprehensive Guide

🗓 Created on 3/4/2025

  • 📜要約
  • 📊ビジュアライズ
  • 🖼関連する画像
  • 🔍詳細
    • 🏷Introduction to Prompt Engineering and Its Importance
    • 🏷Key Skills Required for Prompt Engineers
    • 🏷Structured Online Courses for Prompt Engineering
    • 🏷Hands-On Learning Platforms and Practical Training
    • 🏷Project-Based Learning Approaches
    • 🏷Community Engagement and Peer Learning
    • 🏷Future Trends in Prompt Engineering Training
  • 🖍考察
  • 📚参考文献
    • 📖利用された参考文献
    • 📖未使用の参考文献

📜 要約

Topic and Purpose

The primary focus of this investigation is to explore the diverse methods available for cultivating and training prompt engineers. As organizations increasingly rely on artificial intelligence, especially large language models (LLMs), to drive innovation and efficiency, there is a growing demand for professionals who can effectively engineer prompts. This study aims to objectively outline the various educational strategies and training techniques—from structured online courses to hands-on project-based learning and community engagement—that can help aspiring prompt engineers acquire essential technical, creative, and ethical skills. The purpose is to provide a comprehensive overview that informs stakeholders (educational institutions, corporate trainers, and individual learners) on best practices and emerging trends in the field.

Answer

Based on the context provided, several effective methods for cultivating prompt engineers can be identified. These methods are structured into the following categories:

1. Structured Online Courses and Certifications

Online courses offer a curated curriculum that covers both fundamental theories and advanced techniques in prompt engineering. They provide a comprehensive, guided learning experience often designed by industry experts. Examples include:
  • Prompt Engineering Top Course (Udemy):
    • Comprehensive content covering foundational and advanced prompt design
    • Practical insights from international AI research
    • Source: Udemy
  • ChatGPT Prompt Engineering for Developers (Coursera):
    • Hands-on, project-based learning that emphasizes practical application
    • Focus on using OpenAI API and best practices for LLM interactions
    • Source:
      coursera.org
  • Prompt Engineering Course (U-Miria Inc.):
    • A 1.5-month training course with 16 hours of e-learning and practical exercises
    • Proven techniques that can significantly reduce development workload
    • Source:
      umiria.co.jp
  • IT Engineers’ Guide to Prompt Engineering (Zenn):
    • A structured guide tailored for IT professionals emphasizing prompt design and ethical AI practices
    • Source:
      zenn.dev
The following table summarizes these courses:
Course NamePlatformKey FeaturesSource URL
Prompt Engineering Top CourseUdemyComprehensive curriculum; practical insightsUdemy IT & Software
ChatGPT Prompt Engineering for DevelopersCourseraHands-on project-based learning; use of OpenAI API
coursera.org
Prompt Engineering CourseU-Miria16-hour e-learning; practical exercises; workload reduction
umiria.co.jp
IT Engineers’ Guide to Prompt EngineeringZennDetailed guide for IT engineers; focus on LLM models and techniques
zenn.dev

2. Hands-On Learning Platforms and Practical Training

Practical experience is vital in mastering prompt engineering. Hands-on learning platforms provide real-world scenarios for experimentation, enabling learners to:
  • Practice iterative prompt refinement
  • Engage with sandbox environments to test and optimize AI outputs
  • Work with projects simulating actual job tasks, which reinforces learning through application
Examples include platforms like SynthX and various project-based courses on platforms such as Coursera.

3. Project-Based Learning Approaches

Project-based learning (PBL) facilitates active engagement through real-world challenges. This approach focuses on:
  • Iterative Learning: Continuously refining prompts based on AI feedback.
  • Collaborative Projects: Simulating team dynamics and problem solving, as seen in courses like “Quick Start to Prompt Engineering for Software Developers” and other industry-specific projects.
  • Real-World Application: Applying techniques directly to business or creative scenarios.
A simple flow diagram outlining the PBL approach in prompt engineering is shown below using Mermaid syntax:

4. Community Engagement and Peer Learning

Building a community around prompt engineering enhances learning by enabling:
  • Team Collaboration: Sharing insights, challenges, and best practices in interactive forums and workshops.
  • Peer Reviews and Feedback: Constructive reviews that help in refining prompt creation skills.
  • Access to Resources: Engaging with online guides, discussion groups, and industry-led webinars.
Useful resources include online communities and collaborative platforms, as well as articles like the
zhihu.com
.

5. Continuous Learning and Adaptation

Given the rapidly evolving nature of AI technologies, prompt engineers must commit to lifelong learning. This involves:
  • Staying updated with the latest research, ethical guidelines, and technological advancements in AI and NLP.
  • Participating in ongoing workshops, webinars, and training programs.
  • Adapting prompt engineering techniques to new models and trends, ensuring relevance in a dynamic market.

6. Integration of Ethical and Interdisciplinary Skills

Effective prompt engineering is not just technical but also requires an understanding of:
  • Ethical Considerations: Ensuring that AI outputs are culturally sensitive and free from biases.
  • Interdisciplinary Knowledge: Combining insights from NLP, ML, programming (Python and others), and user experience design.
  • Collaborative Strategy: Working closely with stakeholders to align AI outputs with user needs and organizational goals.

Results and Conclusion

Key Findings:
  • A multifaceted approach is essential for successfully cultivating prompt engineers.
  • Structured online courses provide a robust foundation in both theoretical and practical aspects of prompt engineering.
  • Hands-on and project-based learning are critical for translating theory into practice and for refining prompts iteratively.
  • Community engagement and peer learning foster collaborative problem solving and continuous improvement.
  • Continuous development and ethical training are necessary due to the fast-paced evolution of AI technologies.
Conclusion:
To effectively cultivate prompt engineers, a comprehensive, blended learning strategy should be implemented. This strategy must combine structured educational content, hands-on practical exercises, project-based assignments, and active community engagement. Moreover, continuous learning and the integration of ethical and interdisciplinary skills are crucial to ensure that prompt engineers remain adaptive, innovative, and responsible in their practice. Organizations and educational institutions should therefore invest in diverse training programs that cover these areas, thus preparing professionals to make a significant impact in the rapidly evolving AI landscape.

ビジュアライズ

<!DOCTYPE html>
<html>
<head>
    <title>Prompt Engineering Training Methods</title>
    <script src="https://unpkg.com/mermaid@11.4.0/dist/mermaid.min.js"></script>
    <style>
        body { font-family: Arial, sans-serif; margin: 20px; }
        h1 { color: #333; }
        .chart { margin: 20px 0; }
        .source { font-size: 0.9em; color: #555; }
    </style>
</head>
<body>
    <h1>Methods for Training Prompt Engineers</h1>
    <div class="chart">
        <h2>Training Methods Overview</h2>
        <div class="mermaid">
            graph TD;
                A["Workshops and Training Sessions"] --> B["Hands-on Practice"];
                A --> C["Feedback and Iteration"];
                D["Online Courses"] --> E["Structured Learning"];
                D --> F["Case Studies"];
                G["Community Engagement"] --> H["Forums and Discussion Groups"];
                I["Real-World Projects"] --> J["Practical Application"];
                K["Research and Development"] --> L["Innovative Techniques"];
                A --> D;
                A --> G;
                A --> I;
                A --> K;
        </div>
        <p class="source">Source: <a href="https://www.ibm.com/cn-zh/think/topics/prompt-engineering" target="_blank" rel="noopener noreferrer" class="text-blue-500 underline hover:text-blue-700">IBM</a></p>
    </div>
    <div class="chart">
        <h2>Key Skills for Prompt Engineers</h2>
        <div class="mermaid">
            graph TD;
                A["Natural Language Processing"] --> B["Understanding Language Structures"];
                A --> C["Crafting Effective Prompts"];
                D["Programming Skills"] --> E["Python Proficiency"];
                D --> F["Using NLP Libraries"];
                G["Machine Learning Knowledge"] --> H["Model Training"];
                G --> I["Evaluation Techniques"];
                J["Data Processing"] --> K["Data Analysis"];
                L["Model Evaluation"] --> M["Performance Metrics"];
                A --> D;
                A --> G;
                A --> J;
                A --> L;
        </div>
        <p class="source">Source: <a href="https://www.coursera.org/articles/how-to-become-a-prompt-engineer" target="_blank" rel="noopener noreferrer" class="text-blue-500 underline hover:text-blue-700">Coursera</a></p>
    </div>
    <div class="chart">
        <h2>Future Outlook for Prompt Engineering</h2>
        <div class="mermaid">
            graph TD;
                A["Growing Demand"] --> B["Increased Job Opportunities"];
                A --> C["Integration in Various Industries"];
                D["Continuous Learning"] --> E["Staying Updated"];
                D --> F["Adapting to New Technologies"];
                A --> D;
        </div>
        <p class="source">Source: <a href="https://www.intuit.com/blog/life-at-intuit/how-to-become-a-prompt-engineer/" target="_blank" rel="noopener noreferrer" class="text-blue-500 underline hover:text-blue-700">Intuit Blog</a></p>
    </div>
</body>
</html>

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🔍 詳細

🏷Introduction to Prompt Engineering and Its Importance

画像 1

Introduction to Prompt Engineering and Its Importance

Prompt engineering is an emerging discipline that focuses on optimizing interactions with artificial intelligence (AI) platforms. As the demand for effective communication with AI systems grows, the role of prompt engineers becomes increasingly vital. These professionals specialize in crafting prompts that enable AI to understand and generate desired outputs, thus bridging the gap between human intent and machine comprehension.
The Growth of Prompt Engineering
The global prompt engineering market is projected to grow at a compound annual growth rate (CAGR) of nearly 33% from 2024 to 2030. This rapid growth indicates a promising future for individuals entering this field, as organizations seek to leverage AI technologies for various applications. The significance of prompt engineering lies not only in its potential for career opportunities but also in its ability to enhance AI's effectiveness across multiple domains, including creative content generation, question answering, and personalized recommendations.
Key Responsibilities of Prompt Engineers
Prompt engineers engage in a variety of tasks that extend beyond merely asking questions to AI tools. Their responsibilities include:
  • Evaluating Ethical Implications: They assess prompts for cultural sensitivity and fairness, ensuring that AI outputs do not perpetuate biases.
  • Fine-tuning AI Outputs: By submitting various prompts, they refine AI responses to enhance accuracy and relevance.
  • Collaboration with Stakeholders: They work closely with product teams to develop AI solutions that meet user needs.
  • Monitoring AI Performance: Continuous evaluation of AI systems helps in identifying areas for improvement.
Essential Skills for Success
To thrive as a prompt engineer, individuals must possess a solid foundation in technical knowledge. Key skills include:
  • Programming Proficiency: Familiarity with programming languages such as Python, Java, R, and C++ is crucial for effective prompt engineering.
  • Understanding AI Fundamentals: A grasp of concepts like large language models (LLMs), machine learning (ML), and natural language processing (NLP) is vital for crafting effective prompts.
  • NLP and ML Knowledge: Insight into how NLP processes language and how ML algorithms learn from data is essential for designing prompts that yield accurate responses.
The Importance of Prompt Engineering in AI
Prompt engineering plays a crucial role in enhancing AI's capabilities for several reasons:
  1. Effective Communication: It enables users to articulate their thoughts and preferences clearly, guiding AI to produce desired results.
  2. Content Modification: Users can modify generated content to meet specific criteria, influencing the AI's decision-making process.
  3. Bias Control: Real-time prompt engineering helps manage AI behavior and biases, reducing the risk of generating unfair or discriminatory content.
In summary, prompt engineering is essential for maximizing the performance of AI systems. By understanding how to structure prompts effectively, users can significantly influence the quality and relevance of AI-generated outputs.
Techniques, Models and Applications of Prompt Engineering

Analysis and Insights

The exploration of prompt engineering reveals several underlying trends and insights that are crucial for understanding its significance in the AI landscape.
  1. Emerging Demand for Skills: As organizations increasingly adopt AI technologies, the need for skilled prompt engineers will continue to rise. This trend emphasizes the importance of training programs and workshops that equip individuals with the necessary skills to excel in this field.
  2. Interdisciplinary Nature: Prompt engineering combines technical skills with creativity and ethical considerations. This interdisciplinary approach not only enhances the effectiveness of AI systems but also ensures that they operate within ethical boundaries, addressing concerns related to bias and fairness.
  3. Real-World Applications: The diverse applications of prompt engineering, ranging from creative text generation to personalized recommendations, highlight its versatility. Organizations that invest in prompt engineering can unlock new opportunities for innovation and efficiency.
  4. Continuous Learning and Adaptation: The field of prompt engineering is rapidly evolving, necessitating a commitment to continuous learning. Professionals must stay updated on the latest developments in AI and prompt engineering techniques to remain competitive.
In conclusion, the importance of prompt engineering cannot be overstated. As AI technologies continue to advance, the ability to craft effective prompts will be a critical skill for professionals in the field. Organizations should prioritize training and development in this area to harness the full potential of AI and drive meaningful outcomes. For further reading, you can explore the original sources of this information:
intuit.com
and
xenonstack.com
.
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source logoxenonstack.com
robotic process automation
Platform Engineering
cybersecurity,
xenonstack.ai
What are the Differences Between NLP, NLU, and NLG?
Generative AI for Content Marketing and its Use Cases
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source logogithub.com
copy url
source logozhihu.com
copy url
source logoskillsoft.com
copy url
source logowuzao.com
将人工智能创新带入教育
教育中的提示工程
OpenAI Tokenizer
“Base LLM”
Chat Playground
LLMs 中的指令调整
GitHub Copilot
首次推出了超越 Codex 模型的改进人工智能模型
GitHub Copilot is Getting Better at Understanding Your Code
Inside GitHub: Working with the LLMs behind GitHub Copilot
How to write better prompts for GitHub Copilot
.. GitHub Copilot goes beyond Codex with improved AI model
A Developer's Guide to Prompt Engineering and LLMs
How to build an enterprise LLM app: Lessons from GitHub Copilot
工程师博客
该链接
Completion API
Chat Completion API
OpenAI 中的这个例子
LangChain的这个例子
Prompts For Edu
Open AI
Azure OpenAI
OpenAI Dashboard
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source logointuit.com
compound annual growth rate (CAGR) of nearly 33%
newsletters
Intuit Careers

🏷Key Skills Required for Prompt Engineers

画像 1

Key Skills Required for Prompt Engineers

The role of a prompt engineer has gained significant traction since the advent of generative AI technologies, particularly with the launch of models like ChatGPT in November 2022. As organizations increasingly rely on AI to generate high-quality outputs, the demand for skilled prompt engineers is on the rise. Here are the key skills required for this emerging profession:
  1. Understanding AI Models: A solid grasp of various AI models, including convolutional neural networks (CNNs), transformer architectures, and recurrent neural networks (RNNs), is essential. Knowledge of machine learning fundamentals, such as clustering and classification, is also crucial for effective prompt engineering. This understanding allows engineers to tailor prompts that align with the model's capabilities, enhancing output quality.
    igmguru.com
  2. Natural Language Processing (NLP): Proficiency in NLP techniques and libraries is vital for working with text-based AI models. Key concepts include sentiment analysis, tokenization, and named entity recognition. Familiarity with NLP frameworks like NLTK and spaCy can significantly boost productivity and efficiency in crafting prompts.
    igmguru.com
  3. Data Handling Skills: Skills in data cleaning, feature engineering, and data pre-processing are essential for ensuring high data quality and optimal model performance. This includes the ability to analyze and interpret data effectively, which is crucial for refining prompts based on user feedback and model outputs.
    igmguru.com
  4. Programming Proficiency: Python is the most widely used programming language in AI development. Understanding algorithms, object-oriented programming, and data structures is beneficial for implementing and customizing AI solutions. This technical skill set enables prompt engineers to create more sophisticated and effective prompts.
    igmguru.com
  5. Prompting Techniques: Familiarity with various prompting techniques, such as zero-shot and few-shot prompting, is vital for achieving optimal outcomes. These techniques allow engineers to craft prompts that can elicit the desired responses from AI models, thereby maximizing their utility.
    igmguru.com
  6. Problem-Solving Skills: The ability to troubleshoot and refine prompts is essential, as not all prompts will yield the desired results. Prompt engineers must be adept at analyzing outputs and iterating on their prompts to improve performance continuously.
    igmguru.com
  7. Domain Expertise: Understanding specific domains can enhance the effectiveness of prompts, allowing for more tailored outputs. This expertise enables prompt engineers to create prompts that resonate with the target audience and meet specific needs.
    igmguru.com
  8. Adaptability: The AI landscape is dynamic, and prompt engineers must be willing to learn and adapt continuously. Staying updated on the latest advancements in AI technologies and methodologies is crucial for maintaining relevance in this rapidly evolving field.
    igmguru.com
  9. Communication Skills: Effective communication is necessary for collaborating with cross-functional teams and discussing product development. Prompt engineers must articulate their ideas clearly to ensure that all stakeholders understand the objectives and requirements of the AI systems they are working on.
    igmguru.com
  10. Patience: Developing expertise in prompt engineering takes time and practice, requiring patience and persistence. The iterative nature of prompt crafting means that engineers must be prepared to experiment and learn from failures to achieve success.
    igmguru.com

Analysis and Insights

The skills outlined above highlight the multifaceted nature of prompt engineering. As AI technologies continue to evolve, the complexity of the models will increase, necessitating a deeper understanding of both the technical and contextual aspects of AI. For instance, the projected growth of computer and IT jobs, with an estimated 356,700 job openings annually from 2023 to 2033, underscores the increasing demand for skilled professionals in this field.
igmguru.com
Moreover, the global prompt engineering market is expected to reach a valuation of USD 222.1 million in 2023, with a compound annual growth rate (CAGR) of 32.8% anticipated from 2024 to 2030. This rapid growth indicates that organizations are recognizing the value of effective prompt engineering in maximizing AI outputs.
gsdcouncil.org
In conclusion, aspiring prompt engineers should focus on developing a robust skill set that encompasses both technical and soft skills. Continuous learning, hands-on experience, and a commitment to ethical practices will be essential for success in this dynamic field. As the demand for prompt engineers grows, those who can effectively harness AI's capabilities will play a pivotal role in driving innovation and efficiency across various industries.
Prompt Engineering
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source logoigmguru.com
Gen AI tools
Machine Learning models
State of AI in 2023
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source logocoursera.org
ChatGPT
Google Gemini
What Is Prompt Engineering? Definition and Examples
artificial intelligence
large language model
generative AI
ethics
fairness
[1
[2
What is Natural Language Processing? Definition and Examples
career goals
Blockchain Council
How To Write ChatGPT Prompts: Your 2024 Guide
prompting techniques
chatbot
machine learning
Machine Learning vs. AI: Differences, Uses, and Benefits
data analysis
Bernard Marr
Fei-Fei Li
Andrew Ng (Coursera co-founder)
Ronald van Loon
skills
Cover letter template
LinkedIn profile
Prompt Engineering Jobs: Your 2024 Career Guide
Prompt Engineering for ChatGPT
Prompt Engineering Specialization
What Is Prompt Engineering?
AI Prompt Engineering Isn’t the Future
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source logogsdcouncil.org
predicted valuation
2032
Prompt Engineering Certification.
Prompt engineers play a crucial role
overview of interview questions and answer
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source logoai-island-media.com
indeed
スタンバイ
求人ボックス
indeed
スタンバイ
求人ボックス
BERTによる自然言語処理入門
・GPT2・NLPプログラミング入門
BERTによる自然言語処理
AIパーフェクトマスター講座
実践 自然言語処理
機械学習エンジニアのためのTransformers
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source logoostechnology.co.jp
まずは問い合わせる →
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source logowww.ibm.com
AI
转换器
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🏷Structured Online Courses for Prompt Engineering

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Structured Online Courses for Prompt Engineering

The development of prompt engineers is increasingly supported by structured online courses that provide comprehensive training in this emerging field. Here are some notable courses and their features:
  1. Prompt Engineering Top Course - Udemy
    • This course offers a thorough exploration of prompt engineering, covering both foundational and advanced aspects. It has attracted over 550,952 learners, indicating its popularity among IT and software development enthusiasts.
    • Key Features:
      • High ratings reflecting quality and effectiveness.
      • Tailored content for relevant and engaging learning experiences.
      • Instruction from industry professionals with real-world experience.
    • Learning Outcomes:
      • Skills in prompt design, evaluation, and storage.
      • Insights from international AI research papers.
    • For more details, visit the Udemy course page.
  2. ChatGPT Prompt Engineering for Developers - Coursera
    • This hands-on, project-based course is designed for developers to effectively utilize large language models (LLMs) in applications.
    • Key Learning Objectives:
      • Understanding LLMs and their principles.
      • Best practices for prompt engineering.
      • Experience with the OpenAI API for custom applications.
    • The course emphasizes practical skills through job-related tasks and requires no downloads or installations. More information can be found on the
      coursera.org
      .
  3. The Best 10 Prompt Engineering Courses (March 2025)
    • This overview highlights various courses available for learners at different levels, from beginners to experienced practitioners. Notable mentions include:
      • Google Prompting Essentials: A beginner-friendly course that teaches a 10-step framework for generating AI prompts, emphasizing responsible AI use.
      • Prompt Engineering Specialization (Vanderbilt University): A comprehensive program consisting of three courses aimed at transforming learners into prompt engineering experts.
      • Generative AI: Prompt Engineering Basics (IBM): A 7-hour course providing a solid foundation in prompt engineering with hands-on projects.
    • For a detailed list, visit the Unite AI article.
  4. Prompt Engineering Course - U-Miria Inc.
    • This course is designed for engineers to leverage conversational AI technologies effectively. It features:
      • A standard learning duration of 1.5 months with 16 hours of e-learning training.
      • Practical exercises and proven techniques that have led to an 87% reduction in workload in actual system development scenarios.
    • For more details, visit the
      umiria.co.jp
      .
  5. IT Engineers' Guide to Prompt Engineering - Zenn
    • This book serves as a structured approach for IT engineers to utilize LLM-based generative AI effectively. It covers:
      • Building mental models of LLMs.
      • Prominent prompt engineering techniques tailored for IT engineers.
    • The book is freely available and aims to enhance problem-solving capabilities in the field. More information can be found on the
      zenn.dev
      .

Analysis and Insights

The rise of structured online courses for prompt engineering reflects a significant trend in the AI landscape. As generative AI technologies become more integrated into various industries, the demand for skilled prompt engineers is expected to grow. Here are some key insights:
  • Diverse Learning Opportunities: The variety of courses available caters to different learning styles and levels of expertise. From beginner-friendly courses to advanced specializations, learners can choose programs that best fit their needs. This diversity is crucial as it allows for a broader audience to engage with prompt engineering, fostering innovation and creativity in the field.
  • Practical Application Focus: Many of these courses emphasize hands-on experience and real-world applications. For instance, the project-based learning approach in the Coursera course allows developers to apply their skills immediately, which is essential for mastering prompt engineering techniques. This practical focus not only enhances learning outcomes but also prepares participants for the challenges they will face in the workforce.
  • Integration of AI Ethics: Courses like Google Prompting Essentials highlight the importance of responsible AI use, addressing bias and output evaluation. As AI technologies evolve, understanding the ethical implications of prompt engineering will be vital for professionals in the field.
  • Future Trends: The increasing recognition of prompt engineering's significance suggests that educational institutions may begin to incorporate these skills into their curricula. Additionally, corporate training initiatives could emerge, further driving the demand for structured courses.
In conclusion, structured online courses for prompt engineering are essential for developing the skills needed to navigate the complexities of generative AI. By providing diverse, practical, and ethically-focused learning opportunities, these courses equip learners with the tools necessary to excel in this rapidly evolving field.
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吴恩达 x OpenAI的Prompt Engineering课程专业翻译版
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诸世纪
Zhang Yixin
@Sm1les
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吴恩达 x OpenAI的Prompt Engineering课程专业翻译版
邹雨衡-项目负责人
长琴
玉琳
万礼行
仲泰
魂兮
徐虎
Joye
诸世纪
宋志学
@Sm1les
@LSGOMYP
GithubDaily
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開発
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認定証
アフィリエイト開示
Google プロンプトの基本事項
Google プロンプトの基本事項
プロンプト・エンジニアリング・スペシャライゼーション（ヴァンダービルト大学）
プロンプト・エンジニアリング・スペシャライゼーション（ヴァンダービルト大学）
生成 AI: プロンプト エンジニアリングの基礎 (IBM)
生成 AI: プロンプト エンジニアリングの基礎 (IBM)
生成AIによる迅速なエンジニアリング（edureka）
生成AIによる迅速なエンジニアリング（edureka）
ChatGPT 完全コース: 初心者から上級者まで (edureka)
ChatGPT 完全コース: 初心者から上級者まで (edureka)
ChatGPT の迅速なエンジニアリング (ヴァンダービルト大学)
ChatGPT の迅速なエンジニアリング (ヴァンダービルト大学)
ChatGPT 開発者向けプロンプト エンジニアリング (DeepLearning.AI/OpenAI)
ChatGPT 開発者向けプロンプト エンジニアリング (DeepLearning.AI/OpenAI)
プロンプトエンジニアリング入門コース（LearnPrompting）
プロンプトエンジニアリング入門コース（LearnPrompting）
ChatGPT API (DeepLearning.AI) を使用したシステムの構築
ChatGPT API (DeepLearning.AI) を使用したシステムの構築
生成 AI 基礎スペシャライゼーション (IBM)
生成 AI 基礎スペシャライゼーション (IBM)
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調査のまとめ

Methods for Developing Prompt Engineers

The development of prompt engineers is a crucial aspec...

調査のまとめ

Methods for Training Prompt Engineers

Training prompt engineers involves a multifaceted approa...

🏷Hands-On Learning Platforms and Practical Training

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Hands-On Learning Platforms and Practical Training

The training of prompt engineers is increasingly relying on hands-on learning platforms and practical training methods. These approaches are designed to equip aspiring engineers with the necessary skills to effectively interact with large language models (LLMs) like ChatGPT. Below are some key findings regarding effective methods for training prompt engineers:
  1. Structured Online Courses:
    • Courses such as "ChatGPT Prompt Engineering for Developers" offered by DeepLearning.AI provide a comprehensive curriculum that covers the principles and best practices of prompt engineering. Participants learn to utilize LLMs to build powerful applications efficiently. This course is particularly beneficial for developers looking to enhance their skills in prompt design and optimization. More details can be found
      deeplearning.ai
      .
  2. Hands-On Learning Platforms:
    • Platforms like SynthX offer hands-on experiences in prompt engineering, allowing learners to practice their skills in real-world scenarios. This practical approach helps solidify theoretical knowledge through application, making it easier for learners to grasp complex concepts. More information is available here.
  3. Project-Based Learning:
    • Courses that focus on project-based learning, such as those offered on Coursera, enable learners to engage in job-related tasks with step-by-step instructions. This method fosters a deeper understanding of prompt engineering by applying concepts to tangible projects. An example of such a course can be found
      coursera.org
      .
  4. Specialized Training Programs:
    • Programs like "Prompt Engineering for Developers" on Udemy focus on core principles and techniques for crafting effective prompts. These courses often include automation techniques to improve efficiency in coding tasks, which is essential for modern software development. More details can be accessed
      udemy.com
      .
  5. Community and Peer Learning:
    • Engaging with communities, such as forums or Reddit threads dedicated to prompt engineering, can provide valuable insights and shared experiences. This collaborative learning environment encourages knowledge exchange and problem-solving among peers. More insights can be found
      reddit.com
      .

Analysis and Insights

The methods outlined above highlight a significant trend towards practical, hands-on training in the field of prompt engineering. This shift is essential for several reasons:
  • Real-World Application: The emphasis on hands-on learning and project-based training allows learners to apply theoretical knowledge in practical scenarios. This is crucial in a field where the ability to craft effective prompts directly impacts the performance of AI models.
  • Accessibility: Online platforms and courses make training more accessible to a broader audience. Individuals without extensive programming backgrounds can engage with no-code platforms, allowing them to learn prompt engineering without needing to write complex code. This democratization of knowledge is vital as the demand for prompt engineers grows.
  • Community Engagement: The role of community and peer learning cannot be overstated. Engaging with others in the field fosters collaboration and innovation, leading to a more robust understanding of prompt engineering practices. This collaborative spirit can drive advancements in the field as practitioners share insights and solutions to common challenges.
  • Continuous Learning: Given the rapid evolution of AI technologies, continuous learning is paramount. The integration of AI tools in educational settings will likely enhance learning experiences, allowing students to experiment with prompt engineering in real-time. This adaptability is essential for keeping pace with the fast-changing landscape of AI.
In conclusion, the training of prompt engineers is evolving to include a blend of structured courses, hands-on experiences, and community engagement. As the field grows, innovative training methods will further enhance the skills of future prompt engineers, ensuring they are well-equipped to meet the demands of an increasingly AI-driven world. The emphasis on practical training not only prepares individuals for immediate roles but also fosters a culture of continuous improvement and adaptation in the rapidly changing landscape of AI technologies.
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Understanding Prompt Engineering
significant growth
Python allows you to learn NLP
high-paying prompt engineering
learn Python
understand what AI is
AI Fundamentals Skill Track
Understanding Artificial Intelligence Course
Natural language processing
What is Natural Language Processing?
Introduction to Natural Language Processing in Python
Natural Language Processing Skill Track
GPT
Gemini
LLaMA 3
deep learning
over 175 billion neurons
Transformer architecture
fine-tuning
transfer learning
An Introductory Guide to Fine-Tuning LLMs
Fine-tuning GPT-3 Using the OpenAI API and Python
Fine-tuning LLaMA 2: A Step-by-Step Guide to Customizing the Large Language Model
How to Fine Tune GPT 3.5: Unlocking AI's Full Potential
FLAN-T5 Tutorial: Guide and Fine-Tuning
advanced prompt engineering
learn about LangChain
LangChain for prompt engineers
One-shot Prompting
Chain of Thought
7 AI Projects for All Levels
5 Projects You Can Build with Generative AI Models
7 NLP Projects for All Levels
Kaggle
this free portfolio builder
Certification courses
OpenAI's Sora
Understanding Prompt Engineering
ChatGPT Prompt Engineering for Developers
Developing LLM Applications with LangChain
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Prompt
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Alisha is a Senior Instructional Designer in the computer science domain at Codecademy. Alisha majored in Computer Science and minored in Ethics at the University of Rochester, where she gained experience as a Teaching Assistant. Before joining Codecademy, she taught STEM concepts to secondary students, including as the Lead Algebra Teacher at a Brooklyn charter school.
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吴恩达 x OpenAI的Prompt Engineering课程专业翻译版
邹雨衡-项目负责人
长琴-项目发起人
玉琳-项目发起人
徐虎-教程编撰者
刘伟鸿-教程编撰者
Joye-教程编撰者
高立业
魂兮
宋志学
韩颐堃
陈逸涵
仲泰
万礼行
王熠明
邓宇文
小饭同学
诸世纪
Zhang Yixin
@Sm1les
@LSGOMYP
GithubDaily
contrib.rocks
知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议

調査のまとめ

Methods for Training Prompt Engineers

The development of prompt engineers is crucial in levera...

調査のまとめ

Methods for Training Prompt Engineers

Training prompt engineers involves a combination of educ...

🏷Project-Based Learning Approaches

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Project-Based Learning Approaches

Project-based learning (PBL) is an effective educational strategy that emphasizes active learning through real-world projects. This approach is particularly relevant for training prompt engineers, as it allows learners to engage with generative AI technologies in a hands-on manner. Here, we explore various aspects of project-based learning as it pertains to prompt engineering, drawing insights from several sources.
  1. Hands-On Experience: Courses such as the "Quick Start to Prompt Engineering for Software Developers" emphasize practical labs where participants can format inputs for AI models like GPT-4 and design prompts for business applications. This hands-on experience is crucial for understanding the nuances of prompt engineering, as it allows learners to apply theoretical knowledge in real-world scenarios (
    globalknowledge.com
    ).
  2. Iterative Learning: The process of crafting prompts often requires iterative refinement. As highlighted in the "Mastering Prompt Engineering" guide, developers are encouraged to continuously improve prompts based on AI responses. This iterative approach not only enhances the quality of prompts but also fosters a deeper understanding of how AI models interpret and respond to different inputs (
    medium.com
    ).
  3. Collaborative Projects: Engaging in collaborative projects can significantly enhance learning outcomes. For instance, the "Prompt Engineering for Programmers to Learn Python" course incorporates group activities where participants work together to solve programming challenges using generative AI. This collaborative environment not only promotes knowledge sharing but also simulates real-world team dynamics that prompt engineers may encounter in their careers (
    skillsoft.com
    ).
  4. Real-World Applications: The integration of project-based learning with real-world applications is vital. For example, the "AI Prompt Engineering and RAG for Software Engineers" course focuses on practical techniques that software engineers can apply directly to their work, thereby bridging the gap between academic learning and industry needs (
    udemy.com
    ).
  5. Ethical Considerations: As prompt engineering becomes increasingly important, ethical considerations must also be addressed. Courses like "Prompt Engineering Essentials" discuss the potential biases in AI responses and the importance of crafting fair and unbiased prompts. This focus on ethics ensures that future prompt engineers are not only skilled but also responsible in their use of AI technologies (
    195
    ).

Analysis and Insights

The project-based learning approach for training prompt engineers reveals several underlying trends and insights:
  • Active Engagement: The hands-on nature of PBL fosters active engagement, which is essential for mastering complex skills like prompt engineering. This engagement leads to better retention of knowledge and a more profound understanding of AI interactions.
  • Adaptability: The iterative process of refining prompts encourages adaptability, a crucial trait for prompt engineers who must navigate the evolving landscape of AI technologies. This adaptability is further enhanced through collaborative projects, where diverse perspectives can lead to innovative solutions.
  • Industry Relevance: By focusing on real-world applications, project-based learning ensures that the skills acquired are directly applicable to industry needs. This relevance not only enhances employability but also prepares learners to tackle practical challenges they may face in their careers.
  • Ethical Responsibility: As AI technologies continue to advance, the emphasis on ethical considerations in prompt engineering is paramount. Training programs that incorporate ethical discussions prepare prompt engineers to navigate the complexities of AI deployment responsibly.
In conclusion, project-based learning approaches are instrumental in developing the skills necessary for effective prompt engineering. By combining hands-on experience, iterative learning, collaboration, real-world applications, and ethical considerations, these approaches equip learners with the tools they need to thrive in the rapidly evolving field of AI.
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🏷Community Engagement and Peer Learning

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Community Engagement and Peer Learning

The development of prompt engineering skills is significantly enhanced through community engagement and peer learning. This collaborative approach not only fosters knowledge sharing but also cultivates a supportive environment for aspiring prompt engineers. Here are some key findings and insights from the context provided:
  1. Importance of Team Collaboration
    Successful prompt engineering often requires collaboration among team members. This collaboration allows for the sharing of insights and strategies, which can enhance the overall effectiveness of the prompts being developed. For instance, the report emphasizes that prompt engineers should focus on continuous optimization and adapt their prompts to meet changing usage scenarios and demands. This adaptability is crucial in a rapidly evolving field like AI, where new models and techniques emerge frequently.
  2. Structured Training Programs
    Organizations are encouraged to implement structured training programs that include workshops, mentorship opportunities, and access to resources covering the latest advancements in language models and prompt engineering techniques. Such programs can significantly improve the skill set of prompt engineers, enabling them to design effective prompts that elicit the desired output from language models. The emphasis on training is supported by the notion that ongoing education is vital for keeping pace with the advancements in AI technology.
  3. Peer Learning Opportunities
    Engaging with peers in the field can lead to valuable learning experiences. For example, participating in forums or workshops where prompt engineers can discuss their challenges and successes can provide insights that are not easily gained through solitary study. The sharing of practical experiences, such as the use of specific models like GPT-4o and GPT-4o-mini, can illuminate best practices and common pitfalls in prompt engineering.
  4. Community Resources and Networking
    The availability of community resources, such as online guides and articles, plays a crucial role in the development of prompt engineering skills. Resources like the
    zhihu.com
    and the
    baidu.com
    provide valuable insights into effective techniques and methodologies. Networking within these communities can also lead to collaborative projects that enhance learning and innovation.
  5. Feedback Mechanisms
    Implementing feedback mechanisms within teams can help prompt engineers refine their skills. Regular reviews of prompt designs and outputs can lead to a better understanding of what works and what doesn’t. This iterative process is essential for continuous improvement and can significantly enhance the quality of AI interactions.

Analysis and Insights

The exploration of community engagement and peer learning reveals several underlying trends and implications for the training of prompt engineers:
  • Collaborative Learning as a Catalyst: The emphasis on collaboration suggests that learning in isolation may not be sufficient for mastering the complexities of prompt engineering. Engaging with peers can accelerate the learning curve and provide diverse perspectives that enrich understanding.
  • Adaptability to Change: The rapid evolution of AI technologies necessitates a mindset of continuous learning and adaptability. Prompt engineers must be willing to update their skills and knowledge regularly to remain effective in their roles.
  • Resource Utilization: The availability of comprehensive resources indicates a growing recognition of the importance of prompt engineering in AI development. Aspiring prompt engineers should actively seek out these resources and leverage them to enhance their skills.
  • Feedback as a Learning Tool: The incorporation of feedback mechanisms highlights the importance of reflection in the learning process. Prompt engineers should view feedback not as criticism but as a valuable tool for growth and improvement.
In conclusion, fostering a culture of community engagement and peer learning is essential for the effective training of prompt engineers. By leveraging collaborative opportunities, structured training programs, and community resources, organizations can cultivate a new generation of skilled prompt engineers who are well-equipped to navigate the complexities of AI technologies.
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OpenAI社が推奨するプロンプトのベストプラクティス8つ
各種Prompt Engineeringの日本語実例集（Zero-CoT、mock、ReAct、ToT、Metacog、Step Back、IEPなど）
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🏷Future Trends in Prompt Engineering Training

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Future Trends in Prompt Engineering Training

The landscape of prompt engineering is rapidly evolving, driven by advancements in artificial intelligence (AI) and the increasing demand for skilled professionals in this field. As organizations recognize the importance of effective prompt engineering in optimizing AI models, several key trends are emerging in training methodologies and educational resources.
1. Growth of Online Courses and Certifications
In 2025, a variety of online courses and certifications are available for aspiring prompt engineers. Notable offerings include:
  • Vanderbilt University: Offers multiple courses such as Prompt Engineering and Prompt Engineering for ChatGPT, focusing on skills like Natural Language Processing (NLP) and data analysis.
  • DeepLearning.AI: Provides courses like
    coursera.org
    , which emphasize practical applications of prompt engineering.
  • IBM: Features a course titled Generative AI: Prompt Engineering Basics that covers foundational skills necessary for effective prompt design.
These courses cater to various skill levels, from beginners to advanced learners, and many are offered for free, making them accessible to a broader audience.
2. Emphasis on Practical Applications
Training programs are increasingly focusing on practical applications of prompt engineering. For instance, the course "Learn Prompt Engineering in One Day" covers essential skills needed to effectively communicate with AI through prompts, including various techniques like zero-shot and few-shot prompting. This hands-on approach allows participants to apply their knowledge in real-world scenarios, enhancing their learning experience (
trainocate.co.jp
).
3. Integration of Ethical Considerations
As AI technologies become more pervasive, ethical considerations in prompt engineering are gaining prominence. Training programs now include discussions on the ethical use of data, privacy concerns, and the importance of minimizing bias in AI outputs. This focus on ethics ensures that prompt engineers are not only technically proficient but also socially responsible in their work.
4. Continuous Learning and Adaptation
The field of AI is characterized by rapid changes, necessitating continuous learning for prompt engineers. Courses encourage ongoing skill development and staying updated with the latest research and technologies in AI and NLP. This adaptability is crucial for professionals aiming to remain relevant in a fast-paced environment.
5. Collaborative Learning Environments
Many training programs are fostering collaborative learning environments where participants can share insights and experiences. This peer-to-peer interaction enhances the learning process and allows for the exchange of diverse perspectives on prompt engineering challenges.

Analysis and Insights

The trends in prompt engineering training reflect a broader shift towards a more integrated and responsible approach to AI development. As organizations increasingly rely on AI technologies, the demand for skilled prompt engineers is expected to rise significantly. This trend is supported by the growing number of educational resources and the emphasis on practical applications.
Hidden Trends and Implications
  • Interdisciplinary Skills: The future of prompt engineering will likely require professionals to possess interdisciplinary skills, combining technical expertise with knowledge in ethics, communication, and user experience design. This holistic approach will enhance the effectiveness of AI systems in real-world applications.
  • Data-Driven Decision Making: As prompt engineers become more adept at analyzing and interpreting data, their role will evolve to include data-driven decision-making processes. This shift will empower organizations to leverage AI more effectively, leading to improved outcomes across various sectors.
  • Increased Collaboration with Stakeholders: Prompt engineers will need to collaborate closely with stakeholders, including business leaders and end-users, to ensure that AI systems align with organizational goals and user needs. This collaboration will be essential in developing prompts that yield meaningful and relevant outputs.
In conclusion, the future of prompt engineering training is poised for significant growth and transformation. By focusing on practical applications, ethical considerations, and continuous learning, aspiring prompt engineers can equip themselves with the skills necessary to thrive in this dynamic field. As the demand for AI expertise continues to rise, investing in prompt engineering training will be crucial for both individuals and organizations aiming to harness the full potential of AI technologies.
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Prompt Engineering
Generative AI: Prompt Engineering Basics
Google Prompting Essentials
Prompt Engineering for ChatGPT
ChatGPT Prompt Engineering for Developers
Advanced Prompt Engineering for Everyone
Essentials of Prompt Engineering
Prompt Engineering for Educators
Prompt Engineering for Law
AI Foundations: Prompt Engineering with ChatGPT
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こちら
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まずは問い合わせる →
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这个github repo
这篇Nature文章
Best practices for prompt engineering with OpenAI API
ChatGPT: five priorities for research
Nature
Brown et al. 2020
Min et al. (2022)
思维链提示（CoT）
Wei et al. (2022)
Kojima et al. 2022
Wang et al. (2022)
Wang et al. (2022)
Liu et al. 2022
Liu et al. 2022
Zhou et al., (2022)
AutoPrompt
Prefix Tuning
Prompt Tuning
新模型
这里
发布了ChatGPT API
聊天标记语言
安全漏洞的一种形式
Riley在Twitter上
提示泄漏示例
示例来源
SQL 注入
Simon
另一种解决方法
另一个防御措施
Liu et al. 2022
Weng et al. (2022)
Armstrong 和 Gorman (2022)
Twitter
这个 Twitter 用户
在这里讨论
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https://prompt-engineering.xiniushu.com#概述
https://prompt-engineering.xiniushu.com#意义
https://prompt-engineering.xiniushu.com#受众
https://prompt-engineering.xiniushu.com#亮点
https://prompt-engineering.xiniushu.com#内容大纲

🖍 考察

Research Essence

The core of the inquiry is to determine how to effectively nurture prompt engineers through a variety of educational and training methods. At its heart, this investigation is not merely about teaching technical skills but about equipping individuals to bridge human intent and AI capabilities. By providing robust technical knowledge alongside ethical, collaborative, and iterative problem-solving approaches, organizations can enable prompt engineers to:
• Enhance AI outputs through effective communication and prompt refinement.
• Navigate the rapidly evolving AI landscape through continuous, hands-on learning.
• Address challenges such as cultural sensitivity and bias by integrating ethical training into technical development.
In summary, the true need is to create a training ecosystem that goes beyond superficial content delivery to build a deeply skilled and adaptable workforce capable of leveraging new AI technologies.

Analysis and Discoveries

A comprehensive review of the context reveals several key trends and patterns in training prompt engineers:
  1. Market Growth and Demand
    • The prompt engineering market is projected to grow at a nearly 33% CAGR from 2024 to 2030.
    • This rapid expansion underscores a pressing need for a structured, multidimensional training approach.
  2. Diverse Training Modalities
    • Structured online courses (e.g., Udemy’s popular course with over 550,952 learners and Coursera’s project-based classes) provide a foundation in theoretical aspects and crucial technical skills like NLP, ML, and programming (sources: Udemy,
    coursera.org
    ).
    • Hands-on learning platforms and project-based approaches simulate real-world scenarios that are essential for mastering prompt optimization.
    • Community engagement and peer learning initiatives offer collaborative environments where knowledge sharing accelerates skill acquisition.
  3. Ethical and Interdisciplinary Dimensions
    • The inclusion of ethical considerations in training programs (e.g., Google Prompting Essentials and discussions on minimizing bias) is crucial for responsible AI utilization.
    • Interdisciplinary skills—merging technical prowess with soft skills and ethical reasoning—are emerging as a critical competency for future prompt engineers.

Deeper Analysis and Interpretation

Digging deeper into the findings reveals several layers of “why” behind these trends:
  1. Why Structured Online Courses?
    • They democratize access to knowledge and allow learners across the globe to develop a solid theoretical foundation.
    • These courses are designed to provide sequential learning, starting with the basics of AI, NLP, and ML, and advancing toward specialized prompting techniques. • This structured progression ensures that learners are not overwhelmed while simultaneously preparing them for real-world, unpredictable AI behavior.
  2. Why Emphasize Hands-On and Project-Based Learning?
    • Effective prompt engineering requires iterative refinement; only hands-on projects can illustrate the critical trial-and-error process in crafting effective responses.
    • Engaging in real-life scenarios helps learners build the intuition needed to troubleshoot and optimize prompts over time. • Practical exercises bridge the gap between theoretical knowledge and its application, thereby enhancing retention and transferable skills.
  3. Why Focus on Community and Peer Learning?
    • Collaboration brings diverse perspectives and innovative solutions, which is vital in an area as dynamic as prompt engineering.
    • Peer feedback mechanisms help identify weaknesses in prompt design that may not be evident in isolated study.
    • The shared experience in community forums and workshops fosters continuous learning and ethical accountability in response development.
These deeper layers indicate that a blend of structured learning, practical application, and community engagement is essential to cultivate prompt engineers who are adaptable, ethically aware, and technically proficient.

Strategic Insights

Based on the analysis, several actionable strategies can be implemented to cultivate prompt engineers effectively:
  1. Develop Integrated Training Programs
    • Combine structured online courses (like those from Udemy, Coursera, and DeepLearning.AI) with hands-on labs and collaborative projects to create a well-rounded curriculum.
    • Ensure that the curriculum is continuously updated to reflect the latest advancements in AI, NLP, and prompt engineering techniques.
  2. Emphasize Ethical Training
    • Integrate ethical guidelines into every training module to ensure prompt engineers are equipped to handle issues of bias, cultural sensitivity, and fairness.
    • Organize workshops and seminars that discuss real-world case studies on ethical challenges in AI.
  3. Foster Community Engagement
    • Establish forums, peer-review sessions, and mentorship programs that enable learners to share experiences and solutions.
    • Encourage regular feedback sessions where prompt engineers can iterate on their designs collaboratively.
  4. Invest in Continuous Learning and Adaptation
    • Support ongoing education initiatives such as certifications and advanced courses to keep the workforce updated.
    • Create internal R&D teams focused on prompt engineering innovation and share insights across the organization.
  5. Leverage Industry Partnerships
    • Collaborate with leading educational platforms and AI research organizations to develop best practices and standardized training frameworks.
    • Use data from industry projects to refine training content and ensure it meets current and future demand.

Future Research Proposals

To build on these findings and further enhance training for prompt engineers, the following areas merit additional investigation:
• Development of comprehensive ethical guidelines for prompt engineering, particularly regarding mitigating bias and ensuring fairness in AI outputs.
• Comparative study of the effectiveness of various training modalities (e.g., online courses vs. project-based learning vs. community engagement) in producing proficient prompt engineers.
• Longitudinal research on the career progression of prompt engineers to assess the long-term impact of current training methods on professional development.
• Evaluation of performance metrics for prompt quality and the impact of iterative refinement processes on AI response accuracy.
• Examination of collaborative learning environments and their scalability in fostering innovation and rapid skill acquisition among prompt engineers.
By addressing these areas, organizations and educational institutions can continuously improve training methodologies, ensuring that prompt engineers not only meet current demands but are also prepared for future challenges in the AI landscape.

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🏷 Introduction to Prompt Engineering and Its Importance

How to become a prompt engineer - Intuit Blog
Focus on the core skills of programming, understanding the basics of AI, learning ML and NLP, and going deeper with language models to get the most out of your ...
intuit.comintuit.com
Prompt Engineering | Techniques, Models and Applications
Prompt engineering is a technique used to enhance large language models (LLMs) by refining their prompts and desired outputs.
xenonstack.comxenonstack.com
Prompt-Engineering-Guide-zh-CN/guides/prompts-intro.md at main
提示工程是一种相对较新的学科，专门用于开发和优化提示，以高效地使用语言模型（LM）来处理各种应用和研究主题。提示工程技能有助于更好地理解大型语言模型（LLMs）的能力和 ...
github.comgithub.com
OpenAI 开发系列（七）：LLM提示工程（Prompt）与思维链（CoT）
代码提示工程是指通过设计特殊的代码提示来激发模型的涌现能力。这种方法不需要对模型进行额外的训练，只需要通过设计合适的代码提示来引导模型完成特定任务，代码提示工程 ...
zhihu.comzhihu.com
Courses for Prompt Engineering for Programmers - Track 1 - Skillsoft
Want to use AI programming tools to improve your coding? Learn the basics of prompt engineering and write quality prompts you can use to accomplish tasks.
skillsoft.comskillsoft.com
第四章：提示工程基础- 给初学者的生成式AI教程中文版
在本章中，我们将了解什么是提示工程、为什么它很重要，以及如何针对给定的模型和应用程序目标制定更有效的提示。 我们将了解提示工程的核心概念和最佳实践，并了解交互式 ...
wuzao.comwuzao.com

🏷 Key Skills Required for Prompt Engineers

10 Skills Required to Become A Prompt Engineer - igmGuru
Discover essential skills needed for prompt engineer: problem-solving, critical thinking, coding proficiency, adaptability, and more.
igmguru.comigmguru.com
How to Become a Prompt Engineer: Duties, Skills, and Steps
Prompt engineers need diverse abilities to succeed, including creativity, proficiency with technology, and even an understanding of human psychology.
coursera.orgcoursera.org
Prompt Engineer Skills: A Step-by-Step Learning Path
Prompt engineer skills involve crafting precise and effective inputs for AI language models to generate desired responses.
gsdcouncil.orggsdcouncil.org
プロンプトエンジニアとかいう怪しい職業、ほんとに存在するの ...
必要スキルは何？ · 文章力・言語化力 · 自然言語処理の基礎知識 · AIに関する知識 · プログラミング（主にPython） · 自然言語処理のライブラリ/フレームワークを ...
ai-island-media.comai-island-media.com
プロンプトエンジニアになるには （必要なスキル、注意点）
2.プロンプトエンジニアに必要なスキル · ① 自然言語処理（NLP）の理解 · ② プログラミングスキル · ③ 機械学習およびディープラーニング · ④ データ処理 · ⑤ ...
ostechnology.co.jpostechnology.co.jp
什么是提示工程 - IBM
提示工程师应具备的技能包括熟悉大型语言模型、较强的沟通技巧、阐述技术概念的能力、编程专业知识（尤其是Python）以及对数据结构和算法的牢固掌握。创造力和对新技术效益与 ...
www.ibm.comwww.ibm.com
ChatGPTのプロンプトエンジニアリングとは｜7つのプロンプト例や ...
ChatGPTを使いこなす上でポイントとなるのが、プロンプトエンジニアリングです。この記事では、7つのプロンプト例や記述のコツを紹介していきます。
skillupai.comskillupai.com

🏷 Structured Online Courses for Prompt Engineering

プロンプトエンジニアリングのトップコース - 更新日 - Udemy
Prompt Engineeringの基礎から応用まで徹底解説！プロンプトのコツや評価/保存の方法、プロンプトデザインをマスターして、生成AIを自由自在に！海外のAI論文の内容を掴み、 ...
udemy.comudemy.com
ChatGPT Prompt Engineering for Developers - Coursera
Hands-on, project-based learning. Practice new skills by completing job-related tasks with step-by-step instructions. · No downloads or installation required.
coursera.orgcoursera.org
最も優れた 10 の迅速なエンジニアリング コース (2025 年 XNUMX 月)
幅広いカリキュラム：AIの基礎から高度なプロンプトエンジニアリング技術までを網羅。 ... 7. ChatGPT 開発者向けプロンプト エンジニアリング (DeepLearning ...
unite.aiunite.ai
プロンプトエンジニアリング講座 - 株式会社ユーミリア
コース概要 ; コース名, プロンプトエンジニアリング講座 ; 対象者, エンジニア全般 ; 標準学習期間, 1.5ヶ月 ; 標準学習時間, 16時間 ; 実施方法, eラーニング研修.
umiria.co.jpumiria.co.jp
ITエンジニアのためのプロンプトエンジニアリング - Zenn
はじめに · 第1章 メンタルモデルの構築 · 第2章 代表的なプロンプトエンジニアリング手法 · 第3章 ITエンジニア向けのプロンプトテクニック · 付録.
zenn.devzenn.dev
面向开发者的ChatGPT 提示工程课程｜吴恩达携手OpenAI 教你如何 ...
课程希望帮助开发人员利用LLM 提示工程技术实现文本总结、推理、转换、扩展等应用，并构建自定义的聊天机器人，关键在于“授人以渔”——教大家探索适用于自己 ...
zhihu.comzhihu.com
吴恩达《ChatGPT Prompt Engineering for Developers》课程中文版
本项目是一个面向开发者的LLM 入门教程，基于吴恩达老师大模型系列课程内容，将原课程内容翻译为中文并复现其范例代码，实现中文Prompt，指导国内开发者如何基于LLM 快速、 ...
github.comgithub.com
面向开发者的LLM 入门课程 - GitHub
一个中文版的大模型入门教程，围绕吴恩达老师的大模型系列课程展开，主要包括：. 一、吴恩达《ChatGPT Prompt Engineering for Developers》课程中文版，主要内容为指导 ...
github.comgithub.com
調査のまとめ
#### Methods for Training Prompt Engineers Training prompt engineers involves a multifaceted approa...
調査のまとめ
#### Methods for Developing Prompt Engineers The development of prompt engineers is a crucial aspec...

🏷 Hands-On Learning Platforms and Practical Training

How to Become a Prompt Engineer: A Comprehensive Guide
1. Master Python. Prompt engineers would need a solid understanding of Python programming. Knowing how to code in Python allows you to learn NLP and deep ...
datacamp.comdatacamp.com
Python未経験からはじめる「プロンプトエンジニア」コース
1. Python・数学の前提知識なしからでも効率よくスキルが身につくカリキュラム · 2. 豊富なハンズオンで身につく実践力 · 3. 質問し放題のチャットサポートと1,000問以上のQA ...
skillupai.comskillupai.com
「AI提示工程師」是什麼？工作關鍵特點、必備技能及最新職缺一覽
... 提示工程師，具備良好的程式設計和軟體開發能力是必須的。你需要能夠撰寫高效、可靠且易於維護的程式碼，以實現提示設計。熟悉常用的程式語言，如Python ...
104.com.tw104.com.tw
Python工程师的Prompt：从编程到解决问题的思维转变 - 百度智能云
在培养这些能力的过程中，Python工程师需要不断地学习和实践。学习新的编程知识、掌握新的编程技能、了解新的行业动态，这些都是Python工程师成长所必需的。
baidu.combaidu.com
Prompt Engineering for Software Developer for Code Generation
Prompt engineering is the process of designing prompts that help large language models (LLMs) to generate code.
medium.commedium.com
Prompt Engineering for Code Generation with Examples Codes
Prompt engineering is a process that creates specific prompts or instructions for AI language models to generate code snippets or scripts.
edureka.coedureka.co
Practical Prompt Engineering Masterclass: Hands-On Learning
This workshop covers prompt engineering from scratch to an advanced level. Complete practical demonstration. No FLUFF.
udemy.comudemy.com
プロンプトエンジニアリング実践講座 Powered by エジソン塾
この講座は、AI技術の実践的な利用に焦点を当てたコースです。生成AIの職種や業界別の応用を理解し、GPT技術の基本から応用までを体系的に習得したい個人 ...
tracks.runtracks.run
Learn How to Use AI for Coding - Codecademy
This course will show you how to use ChatGPT for coding by breaking down the basics of prompt engineering for software development.
codecademy.comcodecademy.com
Prompt Engineering for Developers | Udemy
The core principles of prompt engineering and how to craft precise, effective prompts. · Techniques to improve efficiency by automating repetitive coding tasks ...
udemy.comudemy.com
GitHub - logan-zou/prompt-engineering-for-developers-myself ...
#### GitHub - logan-zou/prompt-engineering-for-developers-myself This project serves as an introductory tutorial for developers on Large Language Models (LLMs), based on the course content by Andrew Ng. It translates the original course materials into Chinese and reproduces example codes, guiding domestic developers on how to quickly and efficiently develop powerful applications based on LLMs. The main contents of this project include: - **Prompt Engineering for Developers**: This section is based on Andrew Ng's course "ChatGPT Prompt Engineering for Developers," which introduces developers to constructing prompts and implementing various common functionalities such as summarization, inference, and transformation using the OpenAI API. It serves as a foundational step for LLM development. - **Building a Q&A System with ChatGPT**: Derived from the course "Building Systems with the ChatGPT API," this part guides developers in creating a comprehensive intelligent Q&A system using the ChatGPT API. It covers the entire process of developing a Q&A system based on ChatGPT, introducing a new paradigm for large model development. - **Developing Applications with LangChain**: This section is based on "LangChain for LLM Application Development," providing an in-depth introduction to LangChain, helping learners understand how to use it to develop complete and powerful applications. - **Accessing Personal Data with LangChain**: Building on "LangChain Chat with Your Data," this part expands on LangChain's capabilities for accessing personal data, guiding developers on how to create applications that can access user personal data and provide personalized services. - **Building Generative AI Applications with Gradio**: Based on "Building Generative AI Applications with Gradio," this section instructs developers on how to quickly and efficiently build user interfaces for generative AI using Python. - **Evaluating and Debugging Generative AI**: This part combines the course "Evaluating and Debugging Generative AI" with tools like wandb, providing a systematic approach for developers to effectively track and debug generative AI models. - **Fine-tuning Large Language Models**: Based on "Finetuning Large Language Model," this section discusses how to efficiently fine-tune open-source large language models locally using the Lamini framework. The project emphasizes the importance of learning how to develop applications that integrate LLM capabilities, which is a crucial skill for developers in the evolving landscape of AI. The tutorials are particularly suitable for developers with basic Python skills who wish to enter the field of LLM development. For more information, you can access the online reading [here](https://github.com/logan-zou/prompt-engineering-for-developers-myself) or download the PDF version [here](https://github.com/logan-zou/prompt-engineering-for-developers-myself). The project also acknowledges the contributions of various individuals involved in creating and translating the content, ensuring that it is accessible to a wider audience, particularly Chinese-speaking learners.
github.comgithub.com
調査のまとめ
#### Methods for Training Prompt Engineers Training prompt engineers involves a combination of educ...
調査のまとめ
#### Methods for Training Prompt Engineers The development of prompt engineers is crucial in levera...

🏷 Project-Based Learning Approaches

A developer's guide to prompt engineering and LLMs
Prompt engineering is the art of communicating with a generative AI model. Learn how to build with LLMs and how we built GitHub Copilot.
Mastering Prompt Engineering: A Developer's Guide to Harnessing ...
This guide will explore how prompt engineering works, the types of prompts available, and practical applications for development.
medium.commedium.com
Quick Start to Prompt Engineering for Software Developers
Prompt Engineering offers coders and software developers a competitive edge by empowering them to develop more effective and efficient AI-driven solutions ...
globalknowledge.comglobalknowledge.com
Quick Start to Mastering Prompt Engineering for Software Developers
Quick Start to Prompt Engineering for Coders and Software Developers is a one day course designed to get you quickly up and running with the prompting skills ...
www.shi.comwww.shi.com
Top Prompt Engineering Courses Online - Updated [March 2025]
AI Prompt Engineering and RAG for Software Engineers. Become more productive and use AI technologies more efficiently using Prompt Engineering. Future-proof ...
udemy.comudemy.com
Prompt Engineering for Programmers to Learn Python - Skillsoft
Explore the dynamic realm of Python with the strategic lens of prompt engineering, a methodical approach to mastering Python for developers.
skillsoft.comskillsoft.com
Prompt工程师指南[从基础到进阶篇]：用于开发和优化提示 - 博客园
提示工程技能有助于更好地理解大型语言模型(LLMs) 的能力和局限性。研究人员使用提示工程来提高LLMs在各种常见和复杂任务上的能力，例如问答和算术推理。
cnblogs.comcnblogs.com
Prompt Engineering for Code Generation with Examples Codes
Prompt engineering is a process that creates specific prompts or instructions for AI language models to generate code snippets or scripts.
edureka.coedureka.co
提示工程（Prompt Engineering）和代码生成原创 - CSDN博客
提示工程是人工智能中，特别是在自然语言处理（NLP）领域中的一个重要概念。它涉及设计和编写提示文本（Prompt），以引导模型生成符合特定要求的语言输出。提示 ...
csdn.netcsdn.net
Prompt Engineering Essentials - Anaconda Learning
This hands-on course is designed to teach you the art of crafting, analyzing, and ethically deploying prompts for generative pre-trained transformer (GPT) ...
Courses for Prompt Engineering for Programmers - Track 1 - Skillsoft
Want to use AI programming tools to improve your coding? Learn the basics of prompt engineering and write quality prompts you can use to accomplish tasks.
skillsoft.comskillsoft.com
ChatGPTプロンプトエンジニアリング研修～使いこなすための応用 ...
【公開講座】ChatGPTをはじめとした生成AIで、汎用的に使えるプロンプトの書き方を学びます。また、ChatGPTをさらに便利に活用するための方法をご紹介し、これらを ...
insource.co.jpinsource.co.jp

🏷 Community Engagement and Peer Learning

AIサービスを作成する上で効果のあったプロンプトエンジニアリング
ここで、どれくらい具体的に書くかというのが難しいと思いますが、例えばGitHubCopilotでコードの補助をお願いすると、ソフトウェア開発者という役割定義 ...
qiita.comqiita.com
Prompt提示工程上手指南:基础原理及实践(一) - 知乎专栏
在某些情况下，调整提示可能是提高模型输出质量的唯一途径，特别是在使用那些不允许直接修改内部机制的预训练模型时。 我们再来看看ChatGPT的Prompt工程师给 ...
zhihu.comzhihu.com
Prompt工程师实践经验报告：卓越技术掌握与团队合作贡献 - WebPilot
Prompt工程师的核心工作包括设计出能够精确引导语言模型输出预期答案的提示词（Prompts），以及持续优化这些Prompts以适应不断变化的使用场景和需求。 作为一名Prompt工程师， ...
webpilot.aiwebpilot.ai
如何成为Prompt Engineering 提示工程高手？2023年最全的在线指南
本节介绍为问答和算术推理等任务设计常见和复杂提示的重要最佳做法，并演示为特定任务训练和优化模型的技术。 提示工程指南提供了一套全面的参考材料和工具 ...
zhihu.comzhihu.com
提示词工程(Prompt Engineering) 最佳实践 - 千帆大模型平台- 百度
一种可行的方法是把外部文本当成预训练文本语料学习到模型里，让模型记忆其中的一部分知识，再通过Prompt设计直接进行context-free的问答。 对于确定场景： ...
baidu.combaidu.com

🏷 Future Trends in Prompt Engineering Training

30 ChatGPT Prompts for Software Development Engineers
Learn the 30 best ChatGPT prompts for software engineering. Increase the productivity of your team while improving code quality.
kms-technology.comkms-technology.com
Best Prompt Engineering Courses & Certificates Online [2025]
Master prompt engineering for optimizing AI models. Learn to design effective prompts for machine learning and natural language processing tasks.
coursera.orgcoursera.org
プロンプトエンジニアとは？なるために必要なスキルや資格
1. 基礎教育の取得 ... まず、コンピュータサイエンス、情報工学、データサイエンス、または関連分野での学士号や修士号を取得することが推奨されます。これ ...
ai-souken.comai-souken.com
プロンプトエンジニアになるには （必要なスキル、注意点）
2.プロンプトエンジニアに必要なスキル · ① 自然言語処理（NLP）の理解 · ② プログラミングスキル · ③ 機械学習およびディープラーニング · ④ データ処理 · ⑤ ...
ostechnology.co.jpostechnology.co.jp
Prompt工程师指南[从基础到进阶篇]：用于开发和优化提示 - 博客园
提示工程技能有助于更好地理解大型语言模型(LLMs) 的能力和局限性。研究人员使用提示工程来提高LLMs在各种常见和复杂任务上的能力，例如问答和算术推理。
cnblogs.comcnblogs.com
很全面的提示工程指南（包含大量示例！） 原创 - CSDN博客
提示工程(Prompt Engineering)是一个相对较新的研究方向，用于编写和优化提示，以便各种应用和研究更有效地使用语言模型(laguage model, LM)。 提示工程技能 ...
csdn.netcsdn.net
1日で学ぶプロンプトエンジニアリング | IT研修のトレノケート
本研修では、AIに対する「質問」や「指示」の仕方、すなわち「プロンプトエンジニアリング」を講義と演習を通して、基本から応用までを学ぶことを目的としています。 対話 ...
trainocate.co.jptrainocate.co.jp
简介| 面向开发者的Prompt 工程（官方文档中文版）
吴恩达《ChatGPT Prompt Engineering for Developers》课程中文版，主要内容为指导开发者如何构建Prompt 并基于OpenAI API 构建新的、基于LLM 的应用，主要包括：. 书写Prompt ...
xiniushu.comxiniushu.com

📖 レポートに利用されていない参考文献

検索結果: 162件追加のソース: 0件チャット: 0件
Prompt engineering 101 for developers - Pluralsight
Prompt engineering is a crucial method for you to fine tune and optimize the responses you get from language models.
pluralsight.compluralsight.com
What do I need to learn Prompt Engineering and how long will it take ...
Advanced: Strong knowledge of evaluation techniques and tools, agentic workflows, memory management and RAG, prompt troubleshooting / debugging ...
reddit.comreddit.com
How do I become a prompt engineer (+ tools and sources)? - Quora
Develop Strong Language and Communication Skills: Excellent language skills are crucial for prompt engineers. Focus o. Continue Reading.
quora.comquora.com
The Role of Prompt Engineer: Full Job Description - AltexSoft
The skills of an AI prompt engineer encompass an intricate blend of technical, linguistic, analytical, and creative abilities. Hard skills.
altexsoft.comaltexsoft.com
What six skills do you need to become an AI prompt engineer? - Quora
Programming and Software Development: Proficiency in programming languages such as Python, Java, or C++ is crucial for implementing AI models ...
quora.comquora.com
What is prompt engineering? Definition + skills | Zapier
zapier.comzapier.com
What Does A Prompt Engineer Do? | Job Role & Insights
freelancermap.comfreelancermap.com
Becoming a Prompt Engineer: A Comprehensive 10-Step Guide
datasciencedojo.comdatasciencedojo.com
Prompt Engineering in Software Development: User's Guide
weblineindia.comweblineindia.com
Skills Needed to Become a Prompt Engineer - Blockchain Council
blockchain-council.orgblockchain-council.org
Why Prompt Engineering will replace Programming? - Reddit
It is a program designed to perform specific functions, tasks, or services for users. They also interact with users, are self-contained, and ...
reddit.comreddit.com
Prompt Engineering: Techniques, Applications, and Benefits
What Is Prompt Engineering? Prompt engineering is the process of giving instructions to a generative AI to help produce requested results.
spiceworks.comspiceworks.com
What is prompt engineering and how does it differ from traditional ...
Prompt engineering is a PR-friendly name, for communicating to the AI what you want it to do, which is usually done in a text prompt.
quora.comquora.com
Challenges, Strengths, and Its Place in Software Development's Future
Prompt engineering is also essential in the development of Generative AI (GenAI) applications, particularly as techniques like Retrieval ...
infoq.cominfoq.com
Prompt Engineering: Definition, Practices & Examples (2024)
Top Use Cases of AI Prompt Engineering For Success
a3logics.coma3logics.com
What is Prompt Engineering? [Updated] | GUVI-Blogs
guvi.comguvi.com
What is prompt engineering
servicenow.comservicenow.com
Prompt engineering: The process, uses, techniques, applications ...
leewayhertz.comleewayhertz.com
Maximize Software Efficiency with Prompt Engineering
gamecloud-ltd.comgamecloud-ltd.com
AI Prompt Engineering - Applications, Benefits, Techniques ...
appinventiv.comappinventiv.com
Prompt Engineering- Basics, Uses, Challenges, Examples & Apps
upesonline.ac.inupesonline.ac.in
ChatGPT Prompt Engineering for Developers - DeepLearning.AI
In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications.
deeplearning.aideeplearning.ai
Best prompt engineering course : r/learnmachinelearning - Reddit
Since you're starting a job focused on LLM workflows, check out https://synthx.app. It's a hands-on platform that teaches prompt engineering ...
reddit.comreddit.com
ChatGPT Prompt Engineering for Developers
What you'll learn in this course In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly ...
openai.comopenai.com
Complete Prompt Engineering Course | No Coding Experience
This course is designed to provide a strong understanding of the core concepts and methodologies that are applicable to any Large Language Model (LLM).
promptengineering.orgpromptengineering.org
ChatGPT Prompt Engineering Course for Developers - Simpliaxis
The ChatGPT Prompt Engineering course equips developers with the technical know-how to implement large language models (LLMs). Learn how these LLMs are used ...
simpliaxis.comsimpliaxis.com
10 Best Prompt Engineering Courses for Developers in 2024 | ClickUp
Let's dive right into the 10 most impactful options for both experienced and rookie professionals in 2024. Take a look!
clickup.comclickup.com
Prompt Engineering Training Online | Best Institute in World
promptlearnings.compromptlearnings.com
Certified Prompt Engineer™ Interactive Live Training
globaltechcouncil.orgglobaltechcouncil.org
How To Become a Prompt Engineer (2025): The Path to Success
eweek.comeweek.com
What Is Prompt Engineering? - Skim AI
skimai.comskimai.com
10 Best Prompt Engineering Courses [2025] - GeeksforGeeks
geeksforgeeks.orggeeksforgeeks.org
Master Prompt Engineering | AI Prompting Techniques Course
devlabsalliance.comdevlabsalliance.com
Is Python necessary for prompt engineering? - Quora
Python is an invaluable tool for Quick Assembly Engineers, providing many tools a prompt engineer would want to use.
quora.comquora.com
How do I start a career in Prompt Engineering? - Reddit
A basic understanding of Python is good, but there are No-Code AI platforms to learn that will make coding extinct in years to come.
reddit.comreddit.com
Prompt Engineering: A Practical Example - Real Python
Prompt engineering means constructing your text input to the LLM using specific approaches. You can think of prompts as arguments and the LLM as ...
realpython.comrealpython.com
Will Prompt Enginnering Last? : r/PromptEngineering - Reddit
The way to go right now is learning Python and learning to articulate yourself communication wise , especially working on mental visualization ...
reddit.comreddit.com
Mastering Prompt Engineering in AI: A Guide with Python Examples ...
medium.commedium.com
Top 11 Python Developer Skills in demand
notchup.comnotchup.com
第2章 代表的なプロンプトエンジニアリング手法 - Zenn
この章では、代表的なプロンプトエンジニアリング手法を紹介します。 興味深いことに、科学によって生み出されたLLM自体が科学の対象となり、 ...
zenn.devzenn.dev
第3章 ITエンジニア向けのプロンプトテクニック - Zenn
以下の規則に従って質問に答えてください： 1. 「Python」が「p4n」と表現される規則を分析してください。 2. 同じ規則を「JavaScript」に適用してください ...
zenn.devzenn.dev
プロンプトエンジニアになるには？仕事内容・必要スキル・未経験 ...
Python3エンジニア認定基礎試験は、Pythonの基本的な使用法や仕組み、文法に関する知識を問う試験です。合格には70%以上の正答率が必要で、Pythonの基本 ...
x-hours.comx-hours.com
プロンプトエンジニアとは？仕事内容や年収、必要なスキルも解説
プロンプトエンジニアリングは対話型AIを設計するための手法で、特定のフォーマット（プロンプト）に従ってシステムとやり取りすることに重点を置いています ...
sejuku.netsejuku.net
プロンプトエンジニアリングの例、テクニック、実践的な ... - ClickUp
プロンプトエンジニアリングの例、応用例、AIモデルからより良いアウトプットを得るための様々なテクニックをご確認ください。
clickup.comclickup.com
プロンプトエンジニアリングとは？開発に役立つ使い方 - Qiita
プロンプトエンジニアリングに関する情報が集まっています。現在234件の記事があります。また67人のユーザーがプロンプトエンジニアリングタグをフォローしています。
qiita.comqiita.com
Python経験者向け「プロンプトエンジニア」コース | スキルアップAI ...
skillupai.comskillupai.com
プロンプトエンジニアリングとは？職業に必要なスキルや求人・年収 ...
プロンプトエンジニアとは？必要なスキルや将来性について解説 | テクフリ
techcareer.jptechcareer.jp
AI for Dummies】プロンプトエンジニアリングとは？活用事例や学ぶ方法 ...
nal.vnnal.vn
経済産業省Reスキル講座に生成AI活用に向けたプロンプト ...
prtimes.jpprtimes.jp
記事一覧 | 株式会社シーカーズポート
seekersport.co.jpseekersport.co.jp
プロンプトエンジニアリングとは？ ChatGPTで代表的な12個のプロンプト ...
exawizards.comexawizards.com
【面试题】Python软件工程师能力评估试题（一） 原创 - CSDN博客
1、本能力评估试题旨在全面评估Python软件工程师的知识结构和技能水平，试题会涵盖Python语言的基础知识、Linux操作系统常识、Linux Shell命令、计算机 ...
csdn.netcsdn.net
软件工程师必知的30个ChatGPT Prompts技巧| 爱搜AI工具资源导航站
软件工程师的30 个最佳ChatGPT Prompts 提示 · 1. 规划阶段： · 2. 分析阶段： · 3. 设计阶段： · 4. 开发阶段： · 5. 测试阶段： · 6. 部署阶段：.
aisolink.comaisolink.com
2024年, 提示词工程祝（助）你升职加薪 - 知乎专栏
提示词工程师可以帮助提高模型的输出质量和准确性，发现模型的错误和隐藏功能，以及优化模型的性能和效率。提示词工程师的工作对于确保与人工智能模型的有效 ...
zhihu.comzhihu.com
ConnectAI-E/Prompt-Engineering-Tutior: Pompt 提示词工程师入门 ...
LLM分成两个基本大类：Base-LLM和Instruction-Tuned-LLM。 前者是基础语言模型，基于预训练数据预测下一个单词。后者则针对指令进行微调，使其更可能完成人类的指令。
github.comgithub.com
Prompt Engineer/提示工程师原创 - CSDN博客
什么是提示工程师？简而言之就是为生成式AI（generative AI）撰写文本提示（text prompt）的人，以保证AI正确生成人们所需要的输出成果（output），这一工作流程被 ...
csdn.netcsdn.net
什么是提示工程？ - 生成式人工智能 - AWS
提示工程是指导生成式人工智能（生成式AI）解决方案生成所需输出的过程。尽管生成式人工智能试图模仿人类，但需要详细的说明才能创建高质量和相关的输出。在提示工程中，您 ...
amazon.comamazon.com
博客來-人人都是提示工程師
books.com.twbooks.com.tw
Python：程序员的技能树，决定了一生职业的高度- 编程语言- 亿速云
yisu.comyisu.com
用Agent大模型，我发现了Prompt工程师的10大必备技能_为了实现有效的 ...
csdn.netcsdn.net
AI 提示工程師（Prompt Engineer）正夯！破解5 大核心能力、面試技巧與 ...
cake.mecake.me
提示工程(Prompt Engineering) - chuangzhou - 博客园
cnblogs.comcnblogs.com
AI 提示工程師（Prompt Engineer）正夯！破解5 大核心能力、面試技巧與 ...
cake.mecake.me
软件工程师课程辅导- MaximeSHE - 博客园
cnblogs.comcnblogs.com
你能解決這個測驗嗎? 參加測驗,看看你有多瞭解Python! 準備好 ...
instagram.cominstagram.com
博客來-Python 大數據專案X 工程X 產品資料工程師的升級攻略(第二版)
books.com.twbooks.com.tw
Prompt Engineering For Developers: 11 Concepts and Examples ...
Today, we will cover all the core concepts and principles with very detailed examples of Prompt Engineering.
latitude.solatitude.so
A collection of prompts for generating high quality code... - Reddit
Prompt Ideas for Various Coding Tasks. For implementing a specific algorithm: Implement a [name of algorithm] in [programming language]. Please ...
reddit.comreddit.com
Prompt Engineering Examples, Techniques & Practical Applications
Check out the prompt engineering examples, applications, and various techniques to use it right for better output from AI models.
clickup.comclickup.com
What are the best prompts as developer for writing code? Is there a ...
Using the API is the biggest tip. I would not recommend really long prompt engineering prompts- there is little evidence that they work.
reddit.comreddit.com
ChatGPT for Developers: Efficient Prompt Engineering and ...
medium.commedium.com
Top ChatGPT prompts for programmers with examples
developerupdates.comdeveloperupdates.com
How to get Codex to produce the code you want! | Prompt Engineering
github.iogithub.io
Top 20 ChatGPT Prompts For Software Developers - GeeksforGeeks
geeksforgeeks.orggeeksforgeeks.org
How to optimize context in code generation prompts? - Qodo
qodo.aiqodo.ai
Prompt Engineering for Coding Tasks | by Andrea Valenzuela | TDS ...
medium.commedium.com
Application of prompt engineering — A software engineer's ...
medium.commedium.com
【コピペで使える】ChatGPTプログラミング用プロンプト例37選！
本記事では、プログラミング初心者から上級者まで活用できる実践的なプロンプトテンプレートと、コード生成における制約条件の設定方法、さらには条件分岐 ...
ai-front-trend.jpai-front-trend.jp
エンジニア向けのコピペで使えるChatGPTプロンプト20選
ここでは、ChatGPTにプロンプトを入れて、コードレビューさせたり、コード解説してもらったりと、エンジニアの皆さんの作業効率を格段にあげるもの ...
ai-island-media.comai-island-media.com
プロンプトエンジニアリングの例:具体的な事例を基に解説する
タスクをステップごとに分けて、段階的にプロンプトを与える。 例: ステップ1: 「ソフトウェア開発プロジェクトの目標を設定してください。」 ステップ2: 「設定 ...
styz.iostyz.io
プロンプトの例 - Prompt Engineering Guide
以下では、上手く作成されたプロンプトが異なるタイプのタスクを実行するためにどのように使用されるかについていくつかの例をカバーしています。
promptingguide.aipromptingguide.ai
生成AIのプロンプトの書き方を徹底解説！ 押さえておくべき ...
プログラムコード生成のプロンプト. Pythonのコードを生成するプロンプトの例です。同じように役割から出力形式まで指示し、コードを生成しています ...
hitachi-solutions-create.co.jphitachi-solutions-create.co.jp
PromptyとVS Codeでプロンプトエンジニアリングの効率を爆上げしよう ...
kinto-technologies.comkinto-technologies.com
森崎 修司 on X: "「コード×AIーソフトウェア開発者のための生成AI実践 ...
twitter.comtwitter.com
プロンプトエンジニアリングとは？ChatGPT推奨のコツ8選を紹介 - AI ...
metaversesouken.commetaversesouken.com
プロンプトエンジニアリングとは？活用事例や効果的な利用方法を解説 ...
qed-inc.co.jpqed-inc.co.jp
AI時代に重要になる「プロンプトエンジニアリング」とは？具体例と設計 ...
qbook.jpqbook.jp
コード×AI ーソフトウェア開発者のための生成AI実践入門
gihyo.jpgihyo.jp
プロンプトエンジニアリングとは？~生成AIの性能を最大化する技術 ...
10-5.jp10-5.jp
大模型提示工程之Prompt框架和示例- Eric zhou - 博客园
今天和大家分享一下：大模型提示工程之Prompt框架和示例： TAG框架任务（Task）: 开发一个新的手机应用，旨在帮助用户更好地管理他们的日常健康。
cnblogs.comcnblogs.com
提示词工程(Prompt Engineering) - Picassooo - 博客园
最核心的写一条好prompt的原则就是尽可能清晰、明确地表达你的需求（类比产品经理向程序员提需求）。细分下来，具体原则包括：. 清晰的指令： 足够清晰明确地 ...
cnblogs.comcnblogs.com
提示词（prompt）工程指南（四） - GitHub
提示: 生成10个情感分析例子。将示例归类为积极或消极。生成2个消极的示例和8个积极的示例。使用以下格式进行示例： 问: <句子> 答: <情感>. 输出: 问: 我刚刚得到了最 ...
github.comgithub.com
30个最佳软件工程师的ChatGPT Prompt提示| 爱搜AI工具资源导航站
在规划阶段，ChatGPT可以帮助开发人员定义软件项目的范围和要求，并识别潜在的挑战或机会。首先，请描述您要完成的任务，并提出具体问题，例如：. “What are the potential risks ...
aisolink.comaisolink.com
从1000+模板中总结出的10大提示工程方法助你成为提示词大师！
在这篇文章将与大家分享从1000条提示词模板中总结出来的帮助我们成为更好的提示工程师的10大方法。这里讨论的许多方法可以帮助你基于模型的应用程序接口（ ...
zhihu.comzhihu.com
一分钟读论文：《ChatGPT 提示模式：提高代码质量、重构
这种模式使ChatGPT 从规范中模拟API，从而使开发人员能够立即与API 交互并通过ChatGPT 运行的模拟来测试它。此外，ChatGPT 还可以通过合成示例请求以及提供 ...
github.iogithub.io
关于软件开发的ChatGPT提示词_生成高级java开发工程师的gpt提示词-CSDN博客
csdn.netcsdn.net
AI大模型应用开发】1.0 Prompt Engineering（提示词工程）- 典型构成 ...
aliyun.comaliyun.com
為開發人員提供的prompt engineering：11 個概念和範例 ‍♂️⚡
codelove.twcodelove.tw
GitHub - GitHubDaily/ChatGPT-Prompt-Engineering-for-Developers-in ...
github.comgithub.com
提示工程（prompt engineering)：技术分类与提示词调优看这篇就够了 ...
tencent.comtencent.com
吴恩达《 提示工程》学习笔记-腾讯云开发者社区-腾讯云
tencent.comtencent.com
玩转ChatGPT：吴恩达/OpenAI合作教程《面向开发者的ChatGPT提示工程 ...
csdn.netcsdn.net
大模型开发入门（一）：提示词工程（Prompt Enginnering）
大模型开发- 一文搞懂Prompt Engineering（提示工程） - 53AI-AI生产力 ...
53ai.com53ai.com
解读提示工程（Prompt Engineering）
wolai.comwolai.com
14 Best Online Prompt Engineering Courses [Free & Paid] with ...
Prompt Engineering for Everyone by IBM ... This course combines notes, audio recordings, and hands-on labs to equip you with techniques for ...
learnprompting.orglearnprompting.org
Hands-On Prompt Engineering: Learning to Program ChatGPT Using ...
harvard.comharvard.com
These 6 prompt engineering courses can help you optimize your use ...
fortune.comfortune.com
Advanced Prompt Engineering - by Cameron R. Wolfe, Ph.D.
substack.comsubstack.com
プロンプトエンジニアリングとは？ChatGPTで代表的な12個の ...
プロンプトエンジニアリングは、言語モデルのパフォーマンスを最大限に引き出し、効果的なプロンプトを検証・設計するために重要な役割を果たします。
aismiley.co.jpaismiley.co.jp
初心者のためのPrompt Engineering: 2024 年に学ぶべき場所
ポートフォリオまたは Web サイトを作成して、プロンプトエンジニアリング プロジェクトを紹介し、自分の能力とスキルの実践的な応用を確認します。
miichisoft.commiichisoft.com
【270部】プロンプトエンジニアリング超教科書 ｜hirokaji - note
様々なプロンプトテクニックを学び、具体的なプロンプト例を通じて、実践的なスキルを身につける。 JSON、YAMLなどの構造化データ形式と、自然言語を ...
note.comnote.com
プロンプトエンジニアリング 基礎講座 知識編 - Track Training ...
この講座では、生成AIの活用技術として注目が集まる「プロンプトエンジニアリング」について、考え方だけではなく具体的なノウハウやテクニックを学習し ...
tracks.runtracks.run
生成AI・プロンプトエンジニアリング(ChatGPT) 入門講座 実践編 ...
tracks.runtracks.run
プロンプトエンジニアリングとは？- スキムAI
skimai.comskimai.com
プロンプトエンジニアリングの講座・スクール10選を厳選して紹介 ...
reskilling.comreskilling.com
プロンプトエンジニアリングのエンジニアリングの部分を考える｜分析屋
note.comnote.com
プロンプトエンジニアのためにおすすめのUdemy教材3選｜webdrawer
note.comnote.com
一文了解提示工程（Prompt Engineering） 原创 - CSDN博客
【最全综述】提示工程（Prompt Engineering）：本质、技术与最佳实践全解析！ 提示工程（prompt engineering）则是一门精心雕琢提示的艺术与科学。它 ...
csdn.netcsdn.net
大模型系列：Prompt提示工程常用技巧和实践原创 - CSDN博客
Prompt提示语是使用大模型解决实际问题的最直接的方式，本篇介绍Prompt提示工程常用的技巧，包括Zero-Shot、Few-Shot、CoT思维链、Least-to-Most任务 ...
csdn.netcsdn.net
Azure OpenAI 服务 - Learn Microsoft
虽然提示工程的原则可以在许多不同的模型类型间归纳，但某些模型需要专用的提示结构。 对于Azure OpenAI GPT 模型，目前有两个不同的API，提示工程可以在其中 ...
microsoft.commicrosoft.com
一個半小時學會ChatGPT Prompt 工程的核心概念：吳恩達和OpenAI ...
學習應用開發所需的提示工程最佳實踐； · 發現使用LLM 的新方法，包括如何建構自己的自定義聊天機器人； · 獲得使用OpenAI API 編寫和迭代提示的實踐經驗。
tenten.cotenten.co
博客來-AI提示工程師的16堂關鍵必修課：精準提問x優化提示x有效查詢x ...
books.com.twbooks.com.tw
提示工程（Prompt Engineering）最全综述：本质、技术、最佳实践- 53AI ...
53ai.com53ai.com
AI提示工程師的16堂關鍵必修課：精準提問×優化提示×有效查詢×文字生成 ...
airitibooks.comairitibooks.com
什么是提示工程（Prompt Engineering） - 53AI-AI生产力的卓越领导者 ...
53ai.com53ai.com
AI教程】Prompt提示工程上手指南Prompt基础原理及实践Prompt提示工程简 ...
yumiok.comyumiok.com
博客來-AI提示工程師的16堂關鍵必修課(第二版)：Copilot應用x精準提問x ...
books.com.twbooks.com.tw
什么是提示工程（Prompt Engineering） - AI百科知识| AI工具集
ai-bot.cnai-bot.cn
ChatGPT Prompt Engineering for Developers - course - DataCamp
Dive deep into the principles and best practices of prompt engineering to leverage powerful language models like ChatGPT to solve real-world problems.
datacamp.comdatacamp.com
Prompt Engineering for Developers | Udemy
Whether you want to automate tedious tasks, generate innovative ideas, or improve your coding efficiency, this course equips you with the tools and knowledge to make LLMs work for you.
udemy.comudemy.com
Prompt Engineer Resume Examples & Templates (2025) · Resume.io
resume.ioresume.io
8 Prompt Engineer Resume Examples & Guide for 2025
enhancv.comenhancv.com
AI Prompt Engineer Resume Example | Navigate AI Innovations
kudoswall.comkudoswall.com
10+ Prompt Engineer Resume Samples & Templates for 2025
qwikresume.comqwikresume.com
Become God Like Prompt Engineer With This One Prompt : r/ChatGPT
reddit.comreddit.com
Prompt Engineering for Web Developers | Coursera
coursera.orgcoursera.org
プロンプトの概念をマスターする：LLMを最大限に活用するための ...
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clickup.comclickup.com
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form-mailer.jpform-mailer.jp
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sejuku.netsejuku.net
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and-engineer.comand-engineer.com
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coeteco.jpcoeteco.jp
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bnsn.jpbnsn.jp
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feynman.co.jpfeynman.co.jp
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jianet.or.jpjianet.or.jp
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csdn.netcsdn.net
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bilibili.combilibili.com
04.总结类应用_吴恩达和OpenAI合作推出了免费的Prompt ... - YouTube
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youtube.comyoutube.com
吴恩达联手OpenAI上线面向开发者的Prompt工程免费课程中文字幕 ...
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csdn.netcsdn.net
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在线或现场、由讲师指导的现场即时工程培训课程通过交互式实践练习演示如何在即时工程中使用尖端工具和技术来生成令人印象深刻且逼真的文本和图像。
nobleprog.cnnobleprog.cn
GitHub - 0-yy-0/prompt-engineering-for-developers: 吴恩达大模型 ...
github.comgithub.com
GitHub - QiYongchuan/prompt-engineering-for-developers: 面向开发者 ...
github.comgithub.com
prompt-engineering-for-developers/README.md at main · RAMBOXIE ...
github.comgithub.com
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youtube.comyoutube.com
GitHub - HeteroCat/prompt-engineering-for-developers: 面向开发者的 ...
github.comgithub.com
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juejin.cnjuejin.cn
GitHub - GKDGKD/prompt-engineering-for-developers: 面向开发者的LLM ...
github.comgithub.com
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youtube.comyoutube.com
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csdn.netcsdn.net

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