Chunking Text for Better Embeddings
Learn how to split documents into chunks that embed well, why chunk size and overlap matter, and the strategies that maximize retrieval quality.
Why Chunking Matters
Embedding models have a token limit and produce one vector per input. Feeding a whole document yields a vague, averaged vector. Chunking splits text into focused pieces so each vector captures a specific idea.
The Goldilocks Problem
Chunk size is a balance:
- Too large — diluted meaning, mixed topics in one vector
- Too small — fragments lose context, more vectors to store
Aim for chunks that hold one coherent thought.
All lessons in this course
- Text Embedding Models
- Using Embedding APIs
- Storing & Updating Embeddings
- Chunking Text for Better Embeddings