#RAG

Hybrid Databases Were the Wrong Fix for Requirements RAG

A negative infrastructure result from a requirements RAG lab: Postgres, ParadeDB, and OpenSearch could host useful pieces, but none replaced the current retrieval stack without losing ranking quality or evidence bundles.

The RAG Verifier Learned to Say No. It Still Missed a Hard Requirement Failure.

A held-out requirements RAG experiment: claim-level verification reduced harmful published answers from 35.7% selective risk to 4.0%, but strict human audit still found semantic completeness failures.

Citations Were Not Enough for Safety-Critical Requirements RAG

A negative result from a requirements RAG lab: even with structured records, cited JSON answers, and deterministic guards, the answerer failed the abstention gate when evidence was insufficient.

I Flattened 13,244 Requirements Into Chunks. 97% Lost Their Meaning.

A practical retrieval study over structured automotive requirements: why flatten-and-chunk failed, where ordinary top-k search stopped being the right tool, and how query routing improved both quality and latency.

I Tested a Simple RAG Idea: One Summary per Document. It Was Useful, But Not Enough.

A practical retrieval experiment: can an LLM-generated document summary replace chunk-level search? On a 5,000-document corpus, the answer was measurable — useful signal, clear ceiling, and a better baseline.