Comparison
Chunking vs Embedding
Chunking and Embedding are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Chunking
Always — chunking is upstream of every other RAG decision. Spending 2 hours on chunking strategy commonly beats 2 weeks of prompt tuning.
A 50-page PDF split into 200-token chunks with 50-token overlap → ~150 chunks indexed.
When you would reach for Embedding
Embedding comes up when the question is fundamentally about architecture.
OpenAI's text-embedding-3-large produces 3,072-dim vectors.
Frequently asked
What is the difference between Chunking and Embedding?
Chunking: Chunking is the process of splitting source documents into smaller passages before embedding them for retrieval. Chunk size and boundaries control how relevant retrievals will be. Embedding: An embedding is a list of numbers (a vector) that represents a piece of input — a word, a sentence, an image — in a space where similar things end up close together.
When should I use Chunking vs Embedding?
Always — chunking is upstream of every other RAG decision. Spending 2 hours on chunking strategy commonly beats 2 weeks of prompt tuning. Embedding applies when you are focused on architecture.
Are Chunking and Embedding the same thing?
No. Chunking is agents & tools; Embedding is architecture. They are related but address different parts of the AI stack.