Semantic Chunking
An intelligent chunking strategy for RAG that splits documents based on semantic meaning rather than fixed character counts, keeping coherent topics together.
Why It Matters
Semantic chunking produces more meaningful retrieval units than naive splitting, directly improving RAG answer quality.
Example
Splitting a legal document at section boundaries and topic transitions rather than every 500 tokens, keeping each legal clause intact as a single chunk.
Think of it like...
Like cutting a newspaper by article rather than at arbitrary column positions — each piece contains a complete, coherent idea.
Related Terms
Chunking
The process of breaking large documents into smaller pieces (chunks) before creating embeddings for use in RAG systems. Chunk size and strategy significantly impact retrieval quality.
Retrieval-Augmented Generation
A technique that enhances LLM outputs by first retrieving relevant information from external knowledge sources and then using that information as context for generation. RAG combines the power of search with the fluency of language models.
Embedding
A numerical representation of data (text, images, etc.) as a vector of numbers in a high-dimensional space. Similar items are placed closer together in this space, enabling machines to understand semantic relationships.
Semantic Similarity
A measure of how similar in meaning two pieces of text are, regardless of the specific words used. Semantic similarity captures conceptual relatedness rather than lexical overlap.