auditing-chunking-strategy

What problem does it solve?

It audits RAG chunking strategies so you can stop guessing chunk size, overlap, and splitter choice when retrieval fails on long or structured documents.

Core Features & Use Cases

• Sweeps chunk size and overlap across a held-out QA set to find the best boundary-sensitive retrieval setup.
• Compares splitters such as fixed-token, paragraph, semantic, section-aware, and AST-aware to match corpus structure.
• Surfaces boundary-failure examples and cost tradeoffs so you can justify a production change with evidence. Use it for legal corpora, scientific papers, FAQ collections, codebases, and dialogue logs.

Quick Start

Ask me to audit your RAG chunking strategy and provide the corpus sample, current settings, held-out QA set with source spans, and embedding model so I can run the sweep and recommend the best configuration.