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.
About Pranjul Rathour

Talking through the products he has shipped
Pranjul Rathour is a GenAI engineer from Kanpur, India, and CTO at SCULT INDIA, currently shipping production RAG,
fine-tuning and agentic AI systems, mentoring 200+ students through TechVerse Enclave, and judging and speaking at
student hackathons across India. Updated 2026-09-07.
Reach out if you want to talk GenAI, book a campus session, or invite him to judge:
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Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: [email protected] · Invite me to your campus
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