Evaluate Chunking Choices
Run controlled experiments across chunk size, overlap, boundary rules, and parent expansion. Keep the embedding model, query set, and retrieval settings fixed where possible. Measure Recall@K, context precision, answer support, latency, and index size. Inspect critical failure cases, because average scores can hide lost exceptions.
Store chunker version and parameters with the index. A chunker change generally requires reprocessing and reindexing.
Implementation example
{
"experiment":"chunk-ablation",
"sizesTokens":[300,600,900],
"overlapTokens":[50,100],
"metrics":["recall_at_5","context_precision","answer_support","latency"]
}💡 Versioned experiment records make comparisons reproducible.
Key Takeaways
- Inspect the relevant pipeline stage instead of guessing.
- Preserve source provenance, versions, and access scope.
- Measure quality on representative queries.
Knowledge Check
Why keep the embedding model fixed during a chunking experiment?