auditing-chunking-strategy

To find the best RAG chunk size and overlap, sweep combinations across a held-out QA set with source spans, then score recall@k and answer coverage to identify the optimal retrieval configuration.

RAG retrieval often fails on long-form documents when chunk boundaries split relevant context. Surface boundary-failure examples and compare fixed-token, paragraph, and semantic splitters to match your corpus structure.

Compare text splitters for legal or scientific corpora by running a chunk-size by overlap sweep, measuring recall@k, and analyzing boundary failures to justify the best splitter choice with retrieval evidence.

Yes, you need a held-out QA set with source spans to audit chunking strategies. It provides the ground truth required to calculate recall@k, measure answer coverage, and evaluate boundary failures across splitter configurations.

Evaluate RAG chunking tradeoffs by sweeping chunk size and overlap, comparing splitters like section-aware and AST-aware, and surfacing cost tradeoffs with boundary-failure evidence to justify production changes.

Yes, you can evaluate semantic chunking for codebases and dialogue logs. Compare it against AST-aware and section-aware splitters to see which approach best respects your corpus structure and improves answer retrieval.