RAG Chunking: Mistake That’s Costing You 80% Accuracy

Confess- I know this article will not get that much views but I am too lean to AI from last 2 years. I wanna share the learning side

You spent weeks building your RAG pipeline. Embeddings? Check. Vector DB? Check. LLM fine-tuned? Check. Yet your chatbot still returns garbage when users ask anything beyond FAQ-level queries.

Reason Behind that —

Your chunking strategy!

most RAG failures happen before the LLM ever sees the query. They happen in first few lines of code where you split documents into chunks. Let me fix that for you…

Why Chunking Actually Matters (Beyond the Obvious)

Sure, you know chunks need to fit in your token window. But here’s what breaks in production —

The Context —

Your fixed 500-token chunks split a SQL tutorial right where it explains JOIN syntax. User asks “how to join tables” and gets half an answer.