Chunking
Splitting documents into retrieval-sized pieces — small enough to search precisely, large enough to keep their meaning — before embedding them for search.
Production AI is a system: context, tools, permissions, traces, evals, and feedback loops around the model.
What it is
Retrieval systems don't search whole documents; they search chunks. Chunking is the decision of where to cut: by heading, by paragraph, by token count with overlap, by table row. Each chunk gets embedded and indexed with metadata about where it came from.
Why it matters
Bad chunking is one of the most common causes of bad AI answers. Cut too small and the retrieved text lacks context; too large and the relevant sentence drowns in noise while burning the context budget. Chunk boundaries that split a table or separate a caption from its figure produce confidently wrong citations.
How it works
Strategy follows content type: long-form documents split on structure with token-bounded overlap; transcripts split on speaker turns; tables stay whole or split by row groups; code splits on functions. Chunk size, overlap, and metadata are eval-set parameters — measured against real queries, not guessed.
Related resources
Numeric representations of text (or images) where similar meaning lands close together — the mathematics that lets a system search by meaning instead of keywords.
How an AI agent finds the right document, chunk, or row to ground its answer in — and why the part that matters is the pipeline around the database, not the database itself.
The pattern where an AI agent retrieves relevant context from your data before generating an answer — instead of relying only on what the model learned during training.
Your documents and tables prepared so an AI can actually find the right piece — cut into sensible chunks, labeled, permission-checked, and tested against real questions.