RAG Chunking Strategies

Description

Building Production RAG Systems: Chunking Strategies A comprehensive deep dive into chunking strategies for production RAG systems. Covers the core trade-offs between precision and context, three main chunking approaches, implementation code, and a real-world architecture for routing documents to the right chunking strategy. ================ What you will learn: ================ Why Chunking Matters - Chunking determines what your vector search can find - Wrong chunking causes silent failures — no errors, just bad retrieval - The core trade-off: smaller chunks = better precision, larger chunks = better context Fixed-Size Chunking - The baseline approach: uniform segments of predetermined token length - Typical configuration: 512 tokens with 10-20% overlap - Why overlap matters: concepts span chunk boundaries - Limitations: zero semantic awareness, may split sentences mid-thought - Best for: logs, transcripts, homogeneous content Semantic Chunking - Uses embeddings to identify natural topic boundaries - Algorithm: embed sentences → compute cosine similarity → split at drops - 15-40% retrieval precision improvement on structured documents - Trade-off: computational overhead during ingestion - Best for: technical documentation, research papers, structured reports Document-Aware Chunking - Leverages inherent structure in formatted documents - Markdown: chunk on headers - Source code: chunk on functions and classes - HTML: respect tag boundaries - Parent-child pattern: small chunks for search, large chunks for context Decision Framework - Logs/transcripts → Fixed-size chunking - Technical docs → Header-based or semantic chunking - Source code → Function/class boundaries - Mixed content → Recursive chunking Production Code Patterns - Fixed-size implementation with sliding window - Semantic chunking with similarity threshold - Markdown header-based chunking with metadata - Evaluation function: Recall@k and MRR metrics Real-World Architecture - Document router pattern for multi-format ingestion - Enterprise documentation search: 10,000 engineers, 300K documents - File type → Chunker routing (Markdown → Header, Code → Function, PDF → Semantic, Logs → Fixed) - Embedding pipeline and vector store integration =========== Timestamps: =========== 00:00 - Introduction: Chunking in Production RAG 00:25 - Why Chunking Matters 00:46 - The Core Trade-off: Precision vs Context 01:31 - Strategy 1: Fixed-Size Chunking 02:40 - Strategy 2: Semantic Chunking 04:03 - Strategy 3: Document-Aware Chunking 04:50 - Advanced: Parent-Child Chunking Pattern 05:10 - Decision Framework: Which Strategy to Choose 05:41 - Step 2: Code Examples 05:48 - Code: Fixed-Size Implementation 06:21 - Code: Semantic Chunking Implementation 06:53 - Code: Markdown Header Chunking 07:12 - Code: Evaluation Function (Recall & MRR) 07:43 - Step 3: Real-World Architecture 07:52 - Enterprise Documentation Search System 08:52 - Document Router Pattern ========= About me: ========= I'm Mukul Raina, a Senior Software Engineer and Tech Lead at Microsoft, with a Master's in Computer Science from the University of Oxford, UK #RAG #Chunking #ProductionAI #VectorSearch #LLM #AIArchitecture #RetrievalAugmentedGeneration #Embeddings #AIEngineering #SystemDesign