AI & Engineering · 11 min
Your Vector Database Is Lying to You About Multi-Hop
Dense retrieval hits a proven geometric ceiling on multi-hop questions and fails silently — similarity scores stay confident even when the evidence is missing.
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Dense retrieval hits a proven geometric ceiling on multi-hop questions and fails silently — similarity scores stay confident even when the evidence is missing.
Code is many languages plus an identifier dialect tokenizers shred. Use code-specialized embeddings, identifier-aware retrieval, and structure-aware chunking.
Every vector DB benchmark was run on someone else's data. Benchmark your own vector count, dimensions, and — above all — your real filtering pattern.
Most LLM hallucination is a retrieval failure in disguise. Fix the context first, force citations, and give the model a sanctioned way to say 'I don't know.'