A confidence gate on retrieval scores catches weak matches. It does not catch a strong match on the wrong topic — a question about refund policy retrieving a strongly-matched but irrelevant passage about return shipping addresses.
Add a relevance check, separate from a similarity score
After retrieval, ask the model directly: 'does this context address this question, yes or no?' before generating the final answer. It is a cheap extra call that catches a class of errors similarity scores miss entirely.
- Refuse clearly when the check says no — 'I don't have that in the documents I was given' beats a confident wrong guess.
- Log refusals; a spike on one topic usually means a document gap, not a bug.
- Do not skip this check to save latency on a support or compliance bot — the cost of a wrong answer is higher there.
Builds on what a confidence gate is and citations in RAG answers.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
