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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

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