𝗚𝗲𝗲𝗸 𝗢𝘂𝘁 𝗧𝗶𝗺𝗲: Chunk Happens — Testing different Chunking Strategies for RAG

In building RAG systems, a lot of focus goes into model selection and retrieval methods. But one component that’s often overlooked — yet critically impacts retrieval quality and response relevance — is how documents are chunked before embedding.

Chunking defines the units of context that get passed to the retriever and ultimately influence what the LLM sees. Poor chunking leads to irrelevant matches, incomplete answers, or hallucinations — not because the model is weak, but because it was fed the wrong context.

In this post, I ran a comparative study of 10 commonly used chunking strategies. The goal: identify trade-offs in precision, answer quality, and speed, using a consistent setup with FAISS, all-MiniLM-L6-v2 , and GPT-4.

This was a hands-on experiment, not a theoretical review. I wanted to see how different chunking methods perform in practice — especially under constraints like token limits, latency, and structural document variations.

This is an excerpt — the full article continues on Medium.