Awesome-Prompt-Engineering - This repository includes resources for prompt engineering. π Star if you like it!
See the code
| Explore the revised AI Engineering app, updated from its original 2023 version to reflect how the field has evolved to the present day. |
Prompt engineering is the foundational practice of crafting effective instructions to guide AI models toward accurate, useful, and reliable outputs. As AI systems have evolved, prompt engineering has expanded into context engineeringβthe broader discipline of architecting the full information environment that shapes model behaviour, including system prompts, conversation history, retrieved knowledge, tool definitions, and memory.
Whether you're writing your first prompt or orchestrating complex multi-agent systems, understanding how to communicate effectively with AI models remains essential. This repository provides resources spanning fundamental prompting techniques through to advanced context engineering strategies for production AI applications.
Effective prompt and context engineering requires understanding natural language processing, model capabilities, and user needs. As AI becomes increasingly integrated into scientific discovery, agentic systems, and enterprise applications, these skills are critical for developers, researchers, and AI practitioners. Star, watch, and share this repository to stay current with evolving best practices.π₯
| Name | Description | URL |
|---|---|---|
| Introduction | Timeline of AI from foundations to frontier models. Context for how we got here. | GitHub |
| Basic Prompting | Core techniques: prompt structure, roles, delimiters, output formatting, constraints. | GitHub |
| Intermediate Prompting | Reasoning techniques: few-shot, chain-of-thought, self-consistency, structured reasoning. | GitHub |
| Advanced Prompting | Agentic patterns: ReAct, tool use, prompt chaining, self-reflection, meta-prompting. | GitHub |
| Multi-Modal Prompting | Visual AI: text-to-image, image analysis, video generation, model-specific syntax. | GitHub |
| AI Agents | Building autonomous systems: patterns, orchestration frameworks, tools, memory, debugging. | GitHub |
| AI Tools | Comprehensive guide to tools for building, deploying, evaluating, and governing AI. | GitHub |
| Deep Learning Guide | LLM-relevant concepts: transformers, attention, training, tokenization, inference. | GitHub |
| Articles | Curated reading list organized by topic: agents, RAG, evaluation, safety, production. | GitHub |
| Resources | Learning resources: courses, papers, communities, podcasts, newsletters by role. | GitHub |
| Talks/Slides | Presentations and discussions on AI topics from researchers and practitioners. | GitHub |
| FAQ | Answers to common questions about LLMs, prompting, agents, RAG, and production. | GitHub |
| AI Cheat Sheet | Quick reference: prompt patterns, API parameters, token estimation, cost optimization. | GitHub |
| Glossary | 148 terms and definitions covering LLMs, agents, safety, and context engineering. | GitHub |
| Ethical Charter | Community values: human-centeredness, fairness, transparency, safety, accountability. | GitHub |
The field has evolved from crafting individual prompts to architecting complete context systems. Context engineering encompasses everything that shapes model behaviour at inference time.
| Topic | Description | Date | URL |
|---|---|---|---|
| πeBook | eBook published | 17th April 2023 | URL |
| πeBook | eBook published | 28th April 2023 | URL |
| π»Website | Website is live | 2nd May 2023 | URL |
| πNew Page | Ethical Charter | 14th May 2023 | URL |
| πUpdate | Context Engineering section added | January 2026 | URL |
| π€New Page | AI Agents guide | January 2026 | URL |
| π§ New Page | Deep Learning for LLMs guide | January 2026 | URL |
| πUpdate | Glossary expanded to 148 terms | January 2026 | URL |
| πNew Page | AI Cheat Sheet | January 2026 | URL |
| βNew Page | FAQ updated with modern questions | January 2026 | URL |
| π€Update | Talks/Slides updated with LLM-era content | January 2026 | URL |
| Join the Artificial Intelligence First newsletter today. You'll be kept informed about open source frameworks, carefully selected tutorials, and articles compiled by experts in artificial intelligence. |
| Explore Awesome Data Science. A carefully curated list of awesome Data Science resources. |
We believe that feedback and suggestions are crucial to improving our content and making it more useful for our readers. That's why we encourage you to share your thoughts with us and let us know what you think about our content.
Your feedback can help us identify areas where we can improve and provide more value to our readers. Whether you have suggestions for new topics, ideas for interactive elements, or feedback on our existing content, we would love to hear from you.
By sharing your thoughts and ideas with us, you can help shape the direction of our GitHub repo page and make it a more valuable resource for the prompt engineering community. So please don't hesitate to reach out and let us know what you think. We're looking forward to hearing from you!
This project follows the all-contributors specification. Contributions of any kind are welcome!
See the LICENSE file for license rights and limitations (MIT).
Awesome-Prompt-Engineering - This repository includes resources for prompt engineering. π Star if you like it!
See the code
| Explore the revised AI Engineering app, updated from its original 2023 version to reflect how the field has evolved to the present day. |
Prompt engineering is the foundational practice of crafting effective instructions to guide AI models toward accurate, useful, and reliable outputs. As AI systems have evolved, prompt engineering has expanded into context engineeringβthe broader discipline of architecting the full information environment that shapes model behaviour, including system prompts, conversation history, retrieved knowledge, tool definitions, and memory.
Whether you're writing your first prompt or orchestrating complex multi-agent systems, understanding how to communicate effectively with AI models remains essential. This repository provides resources spanning fundamental prompting techniques through to advanced context engineering strategies for production AI applications.
Effective prompt and context engineering requires understanding natural language processing, model capabilities, and user needs. As AI becomes increasingly integrated into scientific discovery, agentic systems, and enterprise applications, these skills are critical for developers, researchers, and AI practitioners. Star, watch, and share this repository to stay current with evolving best practices.π₯
| Name | Description | URL |
|---|---|---|
| Introduction | Timeline of AI from foundations to frontier models. Context for how we got here. | GitHub |
| Basic Prompting | Core techniques: prompt structure, roles, delimiters, output formatting, constraints. | GitHub |
| Intermediate Prompting | Reasoning techniques: few-shot, chain-of-thought, self-consistency, structured reasoning. | GitHub |
| Advanced Prompting | Agentic patterns: ReAct, tool use, prompt chaining, self-reflection, meta-prompting. | GitHub |
| Multi-Modal Prompting | Visual AI: text-to-image, image analysis, video generation, model-specific syntax. | GitHub |
| AI Agents | Building autonomous systems: patterns, orchestration frameworks, tools, memory, debugging. | GitHub |
| AI Tools | Comprehensive guide to tools for building, deploying, evaluating, and governing AI. | GitHub |
| Deep Learning Guide | LLM-relevant concepts: transformers, attention, training, tokenization, inference. | GitHub |
| Articles | Curated reading list organized by topic: agents, RAG, evaluation, safety, production. | GitHub |
| Resources | Learning resources: courses, papers, communities, podcasts, newsletters by role. | GitHub |
| Talks/Slides | Presentations and discussions on AI topics from researchers and practitioners. | GitHub |
| FAQ | Answers to common questions about LLMs, prompting, agents, RAG, and production. | GitHub |
| AI Cheat Sheet | Quick reference: prompt patterns, API parameters, token estimation, cost optimization. | GitHub |
| Glossary | 148 terms and definitions covering LLMs, agents, safety, and context engineering. | GitHub |
| Ethical Charter | Community values: human-centeredness, fairness, transparency, safety, accountability. | GitHub |
The field has evolved from crafting individual prompts to architecting complete context systems. Context engineering encompasses everything that shapes model behaviour at inference time.
| Topic | Description | Date | URL |
|---|---|---|---|
| πeBook | eBook published | 17th April 2023 | URL |
| πeBook | eBook published | 28th April 2023 | URL |
| π»Website | Website is live | 2nd May 2023 | URL |
| πNew Page | Ethical Charter | 14th May 2023 | URL |
| πUpdate | Context Engineering section added | January 2026 | URL |
| π€New Page | AI Agents guide | January 2026 | URL |
| π§ New Page | Deep Learning for LLMs guide | January 2026 | URL |
| πUpdate | Glossary expanded to 148 terms | January 2026 | URL |
| πNew Page | AI Cheat Sheet | January 2026 | URL |
| βNew Page | FAQ updated with modern questions | January 2026 | URL |
| π€Update | Talks/Slides updated with LLM-era content | January 2026 | URL |
| Join the Artificial Intelligence First newsletter today. You'll be kept informed about open source frameworks, carefully selected tutorials, and articles compiled by experts in artificial intelligence. |
| Explore Awesome Data Science. A carefully curated list of awesome Data Science resources. |
We believe that feedback and suggestions are crucial to improving our content and making it more useful for our readers. That's why we encourage you to share your thoughts with us and let us know what you think about our content.
Your feedback can help us identify areas where we can improve and provide more value to our readers. Whether you have suggestions for new topics, ideas for interactive elements, or feedback on our existing content, we would love to hear from you.
By sharing your thoughts and ideas with us, you can help shape the direction of our GitHub repo page and make it a more valuable resource for the prompt engineering community. So please don't hesitate to reach out and let us know what you think. We're looking forward to hearing from you!
This project follows the all-contributors specification. Contributions of any kind are welcome!
See the LICENSE file for license rights and limitations (MIT).
