# dair-ai/Prompt-Engineering-Guide

🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

Repository: https://github.com/dair-ai/Prompt-Engineering-Guide
Canonical: https://ross.abutalabs.com/products/prompt-engineering-guide
Homepage: https://www.promptingguide.ai/
Language: MDX
License: MIT
License Family: permissive
Topics: deep-learning, prompt-engineering, openai, chatgpt, language-model, generative-ai, agents, ai-agents, llms, agent, rag
Last push: 2026-03-11T20:09:13+00:00

## Health v2 (maintenance only)
Score: 63/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 71, release rhythm 35, longevity 96
- inputs: {"age_days": 1356, "days_push": 175, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 77807, forks 8546 (observed 2026-08-28T04:12:21.659191+00:00)

## What it is
A comprehensive open-source guide by DAIR.AI collecting papers, lessons, notebooks, and resources on prompt engineering, context engineering, RAG, and AI agents for large language models. It is available as a website (promptingguide.ai) with multilingual support and companion courses.

## Use cases
- learn prompt engineering techniques like chain-of-thought and few-shot prompting
- find papers and resources on RAG and AI agents
- study prompt injection and adversarial prompting risks
- get examples of prompts for classification, QA, and code generation
- prepare for building LLM applications with best practices
- learn context engineering for AI agents

## When to choose
- you want a curated, up-to-date reference on prompting techniques and LLM capabilities
- you are a researcher or developer learning to work with LLMs
- you need educational material or lecture resources on prompt engineering, RAG, or agents

## When to avoid
- you need runnable production software or a library to integrate into your codebase
- you want a structured interactive course rather than a reference guide
- you need vendor-specific official documentation

## Facets
- artifact type: learning-resource
- maturity: active
- function: prompt-engineering, rag, agent-framework, nlp, documentation
- domain: large-language-models, artificial-intelligence, tutorials
- platform: cross-platform
- tags: llm, prompting-techniques, context-engineering, educational-guide, openai, chatgpt, notebooks, retrieval-augmented-generation, ai-agents, natural-language-processing, web-server

## Member repositories
- dair-ai/Prompt-Engineering-Guide (main) score 63

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:21.659191+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:14:16.542487+00:00, confidence not recorded.
  - readme: https://github.com/dair-ai/Prompt-Engineering-Guide (fetched 2026-08-28T04:12:21.659191+00:00, sha 7af9bb7070d6)
  - homepage: https://www.promptingguide.ai/ (fetched 2026-08-28T17:42:44.233973+00:00, sha 640fe425fc1f)
  - site_page: https://www.promptingguide.ai/about (fetched 2026-08-28T17:42:44.242767+00:00, sha efcb59c6dc10)
- Data as of 2026-08-30T08:39:29.467469+00:00.