dair-ai

    dair-ai/Prompt-Engineering-Guide

    πŸ™ Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

    79.7K stars
    8.8K forks
    79.7K GitHub watchers
    Updated Nov 2, 2026
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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    The project lists 193 contributors on GitHub.

    Peaked at #12 in the weekly ranking the week of Oct 19, its best in the past year.

    Use Cases & Benefits

    • Provides comprehensive guides, papers, lessons, and resources for prompt engineering, context engineering, retrieval augmented generation, and AI agents.
    • Centralizes up-to-date, curated educational content and practical techniques for optimizing large language model prompts in one accessible resource.
    • Use for learning foundational and advanced prompt engineering techniques to improve LLM task performance and reasoning capabilities.
    • Use for developing and deploying AI agents and retrieval augmented generation workflows with effective prompt design strategies.
    • Use for educators and trainers to deliver structured courses and workshops on prompt engineering and AI agent development.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    MIT

    Permissive: commercial use, modification and redistribution are allowed with attribution.

    Community files

    • README: present

    Watch-outs

    • No commits in the last six months (last push Mar 2026).
    • No GitHub releases; pin a commit or tag instead of a release.

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    From the README

    Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs). Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools.

    Motivated by the high interest in developing with LLMs, we have created this new prompt engineering guide that contains all the latest papers, learning guides, lectures, references, and tools related to prompt engineering for LLMs.

    πŸŽ‰ We are excited to launch our new prompt engineering, RAG, and AI Agents courses under the DAIR.AI Academy. Join Now!

    The courses are meant to compliment this guide and provide a more hands-on approach to learning about prompt engineering, context engineering, and AI Agents.

    Use code PROMPTING20 to get an extra 20% off.

    You can also find the most up-to-date guides on our new website https://www.promptingguide.ai/.

    Excerpt from the dair-ai/Prompt-Engineering-Guide README on GitHub (MIT license). Β© its authors.Read the full README on GitHub

    Daily snapshots

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    Forks

    8.8K+269

    Open issues

    293+17

    Compared with peers in AI Agents

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth

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