Chunking
Splitting documents into pieces before embedding them. Too big and the answer drowns; too small and the context is lost.
01In short
Splitting documents into pieces before embedding them. Too big and the answer drowns; too small and the context is lost.
02Video
slot · videoChunking in 90 secondsadd: GUIDES['chunking'].video
03Guide
slot · guide
A step-by-step guide for “Chunking” goes here. Suggested outline:
- What it is — in one paragraph
- Why it matters in production
- How to do it — 3 to 7 steps
- Pitfalls we see in the field
04Checklist
slot · checklist
Four to eight things a team can tick before go-live.
05FAQ
slot · FAQ
The three questions clients actually ask about “Chunking”.
06Related terms
Data & retrievalRAGBefore answering, the system searches your documents and hands the relevant passages to the model. Answers get grounded in your data without retraining.BasicsEmbeddingA list of numbers that represents the meaning of text or an image, so that similar things end up close together. The basis of semantic search and RAG.
Where we help · Build