Apr 22
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RAG Chunking Strategies: Fixed, Recursive, Semantic & Overlap Explained Most people think RAG fails because of embeddings or vector databases. That’s wrong. Most RAG systems fail before retrieval even starts. The real problem? Chunking. If you split your data the wrong way, your system will never retrieve the right information, no matter how good your model is. In this video, we break down how to chunk documents properly so your RAG system actually works in production. 🚀 What You’ll Learn * Why chunking exists (and why embedding full documents fails) * How chunk size directly impacts retrieval accuracy * (e.g., similarity score: 0.61 vs 0.94 on the same query) * Fixed-size vs Recursive vs Semantic chunking — clear comparison * When and why to use overlap strategies * How to choose the right token size for real embedding models Who This Is For * Product Managers building AI features * Engineers working on RAG pipelines * Founders building AI products * Anyone struggling with bad retrieval results ⏱ Chapters 0:00 Why chunking matters 0:40 What chunking is 1:20 Chunk size comparison 2:30 Overlap strategy 3:10 Fixed vs recursive vs semantic chunking ⚡ Key Insight Bad chunking = bad retrieval = bad answers. Even the best LLM can’t fix missing context. Resources & Links 👉 Full course (AI Coding for PMs):https://maven.com/rajeshpeko/idea2prod 👉 Free weekly live sessions:https://maven.com/rajeshpeko#lightning-lessons 👉 Instructor (Rajesh P) on LinkedIn:https://www.linkedin.com/in/rajeshpeko/ What chunk size are you using right now? And is your retrieval actually working… or just “kind of working”? Drop your setup in the comments 👇 #RAG #Chunking #LLM #AIPM #GenerativeAI #AIEngineering #VectorSearch #AIProducts #Startups #BuildInPublic