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Loading image: Detecting and Reducing Hallucinations - Verlin Labs AI article cover image…Hallucinations are plausible false outputs - not random bugs. Learn to spot high-risk domains, use retrieval and citations, cross-check with independent sources, and build team habits that catch errors before users do.
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Models optimise for fluent continuation. When facts are missing or ambiguous, they invent details that fit the pattern - fake citations, wrong dates, confident nonsense. Risk rises with niche topics, long chains of reasoning, and prompts that demand certainty.
Lowering temperature reduces creativity but does not eliminate hallucinations on factual tasks.
Ask the model to quote sources, then open those sources yourself. Split multi-part claims and verify each. For numbers, recreate calculations in a spreadsheet or calculator tool. Treat the model as a draft author, not a witness.
Show confidence cues honestly: "generated, not verified." Offer one-click source links. Log user corrections - they are gold for prompt and retrieval fixes. Escalate to humans when queries match high-risk templates.
Key takeaway
Hallucinations are structural, not accidental - design for verification with citations, scepticism in high-risk domains, and user-visible uncertainty. Teams that log corrections improve faster than teams that trust fluent answers.
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