Document Chunking: Optimizing RAG Retrieval Pipelines › Document Chunking

In the previous chapters, we built up the core RAG pipeline: retrieving relevant documents and using them to ground an LLM's responses. But we glossed over a critical question: what exactly constitutes a "document" in retrieval? A single PDF might contain hundreds of pages. A webpage might span thousands of words. An internal knowledge base might hold gigabytes of technical documentation, legal contracts, and customer support transcripts. If you embed an entire document as one vector, the resulting embedding must compress an enormous amount of information into a single point in vector space. Important details get diluted, and retrieval precision suffers drastically.

Document chunking solves this problem by splitting large documents into smaller, self-contained pieces before embedding and indexing them. Think of it as deciding how to cut a long book into individual pages before filing them in a library catalog. The finer and more sensibly you cut, the easier it is for someone to find exactly the passage they need. The choice of chunking strategy, chunk size, and overlap between chunks has a surprisingly large impact on RAG quality. A poorly chunked document can bury relevant information inside irrelevant context, while a well-chunked document makes the needed passage easy to retrieve.

The importance of this decision is easy to underestimate. Many practitioners treat chunking as a preprocessing detail and move on quickly to more glamorous components like embedding models or vector databases. But chunking is foundational. It defines the fundamental unit of retrieval: what size and shape of knowledge can the system find and return? Get this wrong and no amount of fine-tuning the downstream components will fully compensate.

Consider a realistic scenario. You are building a RAG system over a collection of scientific papers. A user asks: "What was the sample size in the Jensen et al. study on working memory?" If you have chunked each paper into a single vector, the answer to that highly specific question is buried inside an embedding that also represents the abstract, the literature review, the discussion section, and the conclusions. The probability of retrieving the exact paper is reasonable, but the probability of retrieving the exact sentence containing the sample size is low. If instead you chunk at the paragraph level, the specific methodology paragraph containing the sample size becomes a distinct, findable unit in your index.

This chapter examines chunking strategies, from simple fixed-size splits to semantically aware approaches that respect the natural structure of text. We will implement each strategy, examine how chunk size affects retrieval, build intuition for when to use each approach, and work through a concrete numerical example that illustrates the mechanics of each method. Along the way, we will cover chunk overlap, metadata enrichment, and the special challenges posed by different document types.