dair-ai/Prompt-Engineering-Guide
π Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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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.
General guidance from repository metadata, not legal advice. How we score
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.
π 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/.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
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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|---|---|---|---|---|
| morluto/rea | 450.0K | +22.9K | 75 | 16 days ago |
| vercel-labs/agent-desktop | 21.4K | +8.7K | 75 | 5 days ago |
| milind-soni/OpenMausBot | 20.6K | +8.4K | 75 | today |
| stablyai/orca | 121.4K | +6.7K | 75 | today |
| thedotmack/claude-mem | 115.8K | +4.7K | 75 | today |
| dair-ai/Prompt-Engineering-Guide | 79.7K | β | 35 | β |
| Category median | 115.8K | +8.4K | 75 | β |
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