Why generic chatbots lie confidently
Large language models optimize for plausible text, not factual lookup. Ask a generic bot your clinic's MRI price and it may invent a number that sounds right. In regulated or commercial contexts — healthcare, finance, real estate, food allergen claims — that behavior is unacceptable.
RAG reduces but does not eliminate hallucination. The model can still misread a chunk, blend two sources, or answer when retrieval scores are weak. Production systems need layers beyond "we added a vector database."
Controls we implement on every engagement
Source-only prompting: instruct the model to answer solely from provided context and refuse when context is insufficient. Show citations or internal links for staff-facing bots. Set similarity thresholds — if no chunk clears the bar, return a handoff message instead of guessing.
Maintain a golden question set from real user logs. Run it weekly after document updates. Sabrixa WhatsApp Knowledge Bot applies the same principle for SMBs: uploads are the only truth surface; the bot does not browse the web for menu prices or medical advice.
Human-in-the-loop without defeating automation
Escalate edge cases to humans with retrieved context attached so agents do not restart the conversation. Log failures (empty retrieval, user thumbs-down) to prioritize new documents and FAQ entries — that feedback loop is cheaper than endlessly tweaking temperature.
Hallucination risk drops when scope is honest. A bot that says "I can answer menu and timing questions from our PDF" outperforms one marketed as a general genius that staff cannot trust after one bad quote.