What actually works for cutting AI chatbot hallucinations: 9 lessons from production
Early_Protection6814 · reddit · 2026-10-15
Drawing on the Air Canada case where a tribunal held the airline liable for its chatbot's wrong info, the author ranks nine hallucination-reduction practices by real-world impact:
- Take RAG and the knowledge base seriously — most "hallucinations" are retrieval failures or stale/contradictory source docs
- Give the bot permission to say "I don't know" via explicit system prompt rules and human handoff
- Require citations to source articles, enabling automatic flagging of unsourced answers
- Narrow the scope — a billing bot should do billing only
- Guardrails around the model: validate numbers/dates/URLs against source data, run a second verification pass, and never let the LLM generate transactional data — pull it from APIs
- Low temperature for factual flows; a stronger model is cheaper than one viral screenshot
- Humans in the loop: escalation paths, feedback buttons, root-cause reviews of flagged chats
- Proper evals: real + adversarial + should-refuse questions, rerun on every change, tracking accuracy, groundedness, refusal and escalation rates
- Disclose it's an AI to set expectations
TL;DR: treat your chatbot like a new employee — good training material, clear rules, defined role, supervision, and someone to escalate to.
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