Structure-Aware Semantic Chunking with Title-Chain Prefixes: A 1600-Query Evaluation and the Measurement Trap in Text-Transform Ablations
TL;DR
A three-stage, chunk-side-only semantic chunking pipeline---header-split, semantic merge, and title-chain prefixing---that costs zero additional LLM calls is presented, where the title chain reuses the document's own header hierarchy instead of a generated summary.
Abstract
Chunking is the first and most consequential step in retrieval-augmented generation (RAG): every downstream retrieval decision inherits the chunk boundaries. We present a three-stage, chunk-side-only semantic chunking pipeline---header-split, semantic merge, and title-chain prefixing---that costs zero additional LLM calls: the title chain reuses the document's own header hierarchy instead of a generated summary. On a 1600-query stratified evaluation over a production Markdown knowledge base, the pipeline improves MRR@5 from 0.374 to 0.463 (+23.8%) on the full set and from 0.828 to 0.925 (+11.7%) on the answerable subset (n=563), with dual-annotator Cohen's kappa 0.45 (unweighted, 16,000 score pairs). We then report what we tried and what failed: three query-side or architecture-level follow-ups are design dead ends (unevaluated---no comparable run artifacts), one measured failure (prefix weight decay), and one protocol-level failure that is the paper's central methodological finding. In a same-pool prefix on/off ablation, dual-annotator agreement collapsed from kappa 0.45 to 0.04 under identical prompts---stripping the title-chain context strips the disambiguation signal annotators need to agree on relevance. This measurement trap invalidates a common evaluation practice in chunking research and motivates retrieval-time per-candidate prefix evaluation, the direction we recommend from all our evidence.