Embeddings Are Only as Good as Your Chunks

Retrieval systems are usually blamed on the embedding model, then on the vector database. The real failure is often upstream, in how the documents were split.

An embedding compresses a passage into one vector. If the passage mixes three topics, the vector is an average of three things and matches none of them well. If you split mid-sentence, you strand the subject from its claim. Chunk too large and the signal drowns; chunk too small and you retrieve fragments with no context to stand on.

Good chunking respects structure. Split on real boundaries, such as sections, paragraphs, and list items, not a fixed character count that cuts through the middle of an idea. Keep enough surrounding context that a chunk is intelligible on its own. Overlap where meaning bleeds across a boundary. Attach the source and heading so a retrieved chunk can be cited and trusted.

None of this shows up in a model benchmark. Swap a better embedding model into a badly chunked corpus and you get marginally better nonsense. Fix the chunks and a mediocre embedder suddenly looks smart. Retrieval quality is decided before the first vector is ever computed.