RetrievalAugmentedGeneration

Retrieval-Augmented Generation (RAG) revolutionized AI by grounding LLMs in external knowledge, but traditional flat-chunk retrieval struggles with long, complex documents requiring multi-hop reasoning. RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) solves this by building hierarchical trees of clustered summaries, enabling retrieval across abstraction levels for superior context and accuracy.[1][2] In this zero-to-hero tutorial, you’ll learn RAPTOR’s mechanics, why it outperforms standard RAG, and how to implement it step-by-step with code. We’ll cover pitfalls, tuning, and best practices, empowering developers to deploy production-ready pipelines. ...

HyDE (Hypothetical Document Embeddings) transforms retrieval-augmented generation (RAG) by generating fake, relevance-capturing documents from user queries, enabling zero-shot retrieval that outperforms traditional methods.[1][2] This concise tutorial takes developers from basics to production-ready implementation, with Python code, pitfalls, and scaling tips. What is HyDE and Why Does It Matter? Traditional RAG embeds user queries directly and matches them against document embeddings in a vector store, but this fails when queries are short, vague, or mismatch document styles—like informal questions versus formal passages.[4][5] HyDE solves this by using a language model (LLM) to hallucinate a hypothetical document that mimics the target corpus, then embeds that for retrieval.[1][2] ...