Chunking in RAG — You're Splitting Your Documents Wrong

RAG Chunking: 8 Strategies That Actually Work (With Code) Your RAG system is only as good as how you split your documents. Chunk too small — you lose context. Chunk too big — you drown in noise. Chunk at the wrong boundary — you destroy meaning entirely. In this video, I break down the 8 chunking strategies every AI engineer needs to know — from the simplest baseline to production-grade semantic splitting. Each method includes the math, Python code, real-world examples, and exactly when to use it. 📚 What You'll Learn: • Why chunking is the most underrated step in RAG • The chunk size sweet spot (with the math to prove it) • Why overlap prevents information loss at boundaries • 8 methods: Fixed-Size → Sentence → Recursive → Semantic → Structure-Based → Parent-Child → LLM-Based → Code-Specific (AST) • How to handle tables, images, and PDFs without losing data • 5 common mistakes that silently kill retrieval quality • Domain-specific recommendations (Healthcare, Legal, Code, Finance) ⏱️ Timestamps: 00:00 — Cold Open 00:15 — Why Chunking Is the Most Underrated Step in RAG 02:15 — The Goldilocks Problem: Chunk Size Tradeoffs 03:45 — Overlap Strategy: Why Those 50 Tokens Matter 05:00 — Method 1: Fixed-Size Chunking 06:15 — Method 2: Sentence-Based Chunking 07:30 — Method 3: Recursive Text Splitting (LangChain Default) 09:30 — Method 4: Semantic Chunking (Split by Meaning) 11:45 — Method 5: Document Structure-Based Chunking 13:00 — Method 6: Parent-Child Hierarchical Chunking 14:30 — Method 7: LLM-Based Chunking 15:30 — Method 8: Code-Specific AST Chunking 16:30 — Handling Tables, Images, and PDFs 17:30 — 5 Common Mistakes That Kill Your RAG 18:45 — The Decision Framework: Which Method to Use 19:30 — 5 Rules for Production Chunking 🔗 Resources: • LangChain Text Splitters: https://python.langchain.com/docs/how... • Sentence Transformers: https://www.sbert.net/ • Unstructured Library: https://github.com/Unstructured-IO/un... • Full script & code: [link to your GitHub/blog] 📌 Related Videos: • RAG Similarity Methods: 10 Methods That Actually Work → [link] • Building a Production RAG Pipeline → [link] • Vector Databases Compared → [link] 💬 Drop a comment — What chunking strategy are you using in your RAG pipeline? #RAG #Chunking #LLM #AI #MachineLearning #LangChain #NLP #VectorDatabase #AIEngineering #SemanticChunking