nirdiamant/rag_techniques

RAG_Techniques is a collection of tutorial notebooks that show different ways to build and improve Retrieval-Augmented Generation systems. Retrieval-Augmented Generation, or RAG, is a pattern where a generative AI model is paired with a search step over your own documents: when someone asks a question, the system first fetches the most relevant passages from a knowledge base and then feeds them into the model along with the question, so the answer is grounded in your data instead of only the model's general training. The repository is meant as a hub of techniques for making this pattern more accurate, more efficient, and richer in context. The README frames the project as a curated collection of advanced techniques aimed at researchers and practitioners who want to push what RAG can do. Each technique comes with its own detailed notebook tutorial, so a reader can open a notebook, follow the explanation, and run the code to see how that particular method behaves on real data. The overall introduction is general, the notebooks themselves are where the specifics of each method live. Most of the README's visible portion is promotional material around the project rather than the techniques themselves: a tie-in book sold on Amazon (described as the extended version of the repo), a companion Prompt Engineering book on a Kindle deal, a newsletter signup, sponsor logos, and links to a subreddit, Discord, LinkedIn, and Twitter. The author, Nir Diamant, also cross-promotes a related repo called Agents Towards Production, focused on building production-grade generative-AI agents. Someone would use this repository when they are building a question-answering system, a chatbot over internal documents, or any product that needs an AI to reason over a body of text the model was not originally trained on, and they want to learn beyond the basic retrieve-then-generate setup. It is also useful for comparing approaches before committing to one in production. The code is written as Jupyter notebooks in Python, and the topics list points to embeddings, LangChain, LlamaIndex, and OpenAI as part of the toolset used.