Why Your RAG Gives Wrong Answers and 4 Chunking Strategies to Fix It - LangChain Text Splitters

Learn four advanced chunking strategies to fix wrong answers in your RAG (Retrieval-Augmented Generation) system through this 18-minute technical tutorial. Discover why naive text splitting destroys context and breaks semantic meaning, then explore structure-aware chunking that respects document organization, semantic chunking that understands meaning beyond just text, LLM-driven chunking for handling complex requirements, and contextual enrichment techniques that connect isolated chunks. Follow along with practical implementations using LangChain text splitters, starting with basic character text splitting and progressing through markdown heading-based splitting, semantic chunking methods, and LLM-driven approaches. Examine a bonus technique featuring visual grounding and get guidance on selecting the most appropriate chunking strategy for your specific use case. Access complete source code, written guides, and additional resources including context rot research and chunking technique evaluation reports to deepen your understanding of effective context engineering in RAG systems.