Prompt-Engineering-Guide open source analysis

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

Project overview

⭐ 68798 · MDX · Last activity on GitHub: 2025-12-29

GitHub: https://github.com/dair-ai/Prompt-Engineering-Guide

Why it matters for engineering teams

Prompt-Engineering-Guide addresses the practical challenge of effectively designing prompts for large language models and AI agents, a critical task for software engineers working with generative AI technologies. It is particularly suited for machine learning and AI engineering teams who need a reliable, production ready solution to improve model responses and integrate AI-driven features into their products. The repository compiles a comprehensive set of guides, papers, and notebooks that support real-world applications, making it a valuable open source tool for engineering teams focused on prompt and context engineering. While mature and well-maintained, it is not the right choice for teams seeking out-of-the-box AI models or fully managed AI services, as it requires hands-on expertise and integration effort.

When to use this project

This project is a strong choice when teams need to deepen their understanding of prompt engineering and optimise interactions with language models in production environments. Teams should consider alternatives if they require turnkey AI solutions or prefer fully hosted platforms with minimal setup.

Team fit and typical use cases

Machine learning engineers and AI specialists benefit most from this resource, using it to refine prompts, build retrieval augmented generation (RAG) workflows, and develop AI agents for various applications. It commonly appears in products involving natural language understanding, conversational AI, and custom AI integrations where a self hosted option for prompt engineering is essential.

Best suited for

Topics and ecosystem

agent agents ai-agents chatgpt deep-learning generative-ai language-model llms openai prompt-engineering rag

Activity and freshness

Latest commit on GitHub: 2025-12-29. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.