Break It Down Right - Effective Chunking Strategies
Master the most critical step in RAG - chunking. Learn to move beyond simple splitting with structure-aware, semantic, and LLM-driven chunking techniques to build a knowledge base that powers context-aware AI.
Your RAG system generates answers, but when you test complex queries, the results are disappointing. The model misses key information, returns irrelevant passages, or seems confused about context. After debugging countless RAG systems, here's what I've learned: the problem is almost never the LLM. It's the chunking. Naive, fixed-size chunking is like tearing a book into random 500-word sections and expecting someone to understand the plot. This approach breaks semantic context, separates related ideas, and feeds your retrieval system a fragmented, confusing view of your knowledge base.
You'll learn why most RAG systems fail1 at the chunking stage and how to engineer chunks that preserve context, maintain relationships, and actually help your retrieval system find the right information. We'll start with structure-aware approaches that respect document boundaries, move to semantic chunking that understands meaning, and implement LLM-driven strategies that intelligently deconstruct any document type. By the end, you'll have the chunking toolkit that separates working prototypes from production-ready RAG systems.
Tutorial Goals
- Understand the critical role of chunking in RAG performance and its core trade-offs
- Implement structure-aware chunking using document elements like headers and paragraphs
- Leverage embedding models to create semantically coherent chunks based on meaning
- Build a custom, LLM-driven chunking pipeline to intelligently segment complex documents
- Enrich document chunks with LLM-generated contextual summaries to improve retrieval accuracy.
- Evaluate the trade-offs between different chunking methods for various use cases