Semantic Chunking for RAG: Test the Boundary Before Changing the Model

Semantic chunking places boundaries using changes in meaning, often estimated from sentence representations. It can help when headings poorly reflect topic changes, but a semantic boundary can still separate a rule from its exception.

Evaluate it against a simple structural baseline on your own documents. A published score from another corpus cannot establish the best threshold, chunk size, or embedding model for your workload.

Define what a complete answer span contains

Before choosing boundaries, label the passages needed to answer representative questions. A definition may fit in one sentence. A troubleshooting answer may require a prerequisite, three steps, and a warning. Preserve the entire dependency when judging the chunk.

For example, “reset the connection” is incomplete if the next paragraph says not to do so during an active transfer. A chunk that retrieves perfectly by topic can still omit the condition needed for a safe, correct answer.

Compare more than one budget

Keep a fixed number of retrieved chunks for one comparison, then repeat under a fixed context-token budget. Larger chunks carry more text, so a fixed top-k comparison alone can give them an unfair advantage.

Record chunk count, length distribution, overlap, and duplicate passages. Measure ingestion work too: a more elaborate boundary algorithm can cost more to run and make small document revisions change many downstream chunks.

Distinguish segmentation from enrichment

Changing where text is split is different from adding document context to each chunk. Anthropic's contextual retrieval work describes the latter approach. Test the changes separately before combining them, so a gain can be attributed to the right intervention.

Parent-child retrieval is another separate option: retrieve a focused passage, then supply a permitted surrounding section. It can recover missing context without requiring every indexed unit to be large.

Keep a failure gallery

Save examples involving tables, repeated headings, lists, code blocks, exceptions, and topic transitions. Label whether each failure came from a split boundary, extraction, ranking, or generation. “Semantic chunking did worse” is less useful than knowing it repeatedly separated a condition from its instruction.

For mixed collections, use the document-type chunking matrix to separate prose, tables, transcripts, and code before comparing results.

The chunking strategies guide covers the wider choice. For a managed retrieval comparison, try Supermemory with the same question set and inspect the returned evidence. This article proposes an evaluation; it does not present newly measured chunking benchmarks.