dair-ai / Prompt-Engineering-Guide
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
Why this job: matched by what the repo talks about: rag, agent, paper, notebook
What it does
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.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Inactive
- Last commit 8 months ago
- Stars
- 80,006
- +161 this week
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- MDX
- 94% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 28% closed
- 189 still open
- Pull requests
- 342 merged
- 109 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- MDX 94%
- Jupyter Notebook 4%
- TypeScript 1%
- HTML <1%
Topics
- agent
- agents
- ai-agents
- chatgpt
- deep-learning
- generative-ai
- language-model
- llms
- openai
- prompt-engineering
- rag
Project record
- JobRAG and search
- StatusListed
- Commits1,589
- Forks8,783
- Watching752
- Stars in 28 days+904
- CreatedDec 16, 2022
- First seen by usNov 7, 2026
- CheckedNov 7, 2026

