Why "How You Cut" Matters as Much as "What You Cut" In the first three articles, we built a working RAG pipeline and tuned the core parameters. But if you look closely at the retrieval results, you may notice a strange phenomenon: The answer is clearly in the document, yet the Retriever can't find it. Or it finds it, but the answer is cut in half — the LLM only sees the first half of the sentence. The problem usually lies in the chunking step. Chunking is essentially an information splitting strategy — how you divide a 500-page book, how large each piece is, and where you make the cuts directly determines whether the reader (here, the Retriever) can quickly find what they need. In this article, we'll process the same technical document with four different strategies so you can see the dramatic differences that "how you cut" makes. 📎 Source Code : All experiment code is open-sourced at llm-in-action/04-chunking-strategies . Clone it to reproduce the results.…