Chunking Before Embedding

Chunking

Part of: Embeddings & Semantic Search

Try to embed an entire 80-page manual as one string and the result is useless. The model averages everything into one fuzzy vector that points nowhere in particular, and your search returns the whole manual for every query. The fix is the unglamorous step that quietly decides whether a RAG system works at all: chunking . Split the document into bite-sized pieces first, then embed each piece on its own. What it is Chunking is breaking a long document into smaller passages, called chunks , before embedding, so each vector represents one focused idea instead of a blur of many. A chunk might be a paragraph, a few sentences, or a fixed number of words. Each chunk gets its own embedding and its own row in the index. When a query comes in, you retrieve the matching chunks, not whole documents. How it works The simplest scheme is fixed-size: pack words into a chunk until you hit a size cap, then start a new chunk. Greedy and predictable. Two dials control quality. Chunk size sets how much meaning each vector carries: too big and the vector blurs many topics together; too small and a single idea gets split across chunks so neither one is complete. A few hundred tokens is a common sweet spot

Challenge: Fixed-Size Word Chunker