How to reduce AI hallucinations by cross-checking models
A hallucination is a confident wrong answer. The cheapest detector is a second model that wasn't told the first one's answer.
Why models make things up
A language model predicts plausible text, not verified truth. When it doesn't know something, the fluent guess and the correct answer look identical from the outside — which is exactly what makes hallucinations dangerous. The model isn't lying; it has no built-in sense of when it's wrong.
Cross-checking, cheaply
- 1Ask your question of one model and note the answer.
- 2Ask the same question of a different model, without showing it the first answer.
- 3Where they agree, confidence goes up. Where they diverge, treat the claim as unverified.
- 4For anything that matters, confirm the divergent claim against a primary source.
This won't catch a mistake both models share, but it catches the single-model hallucination that most often slips through.
Automating the second opinion
Doing this by hand for every question is tedious, which is why it rarely happens. Bizwax's AI Consensus runs the comparison for you — same prompt, several models, differences surfaced — so the cross-check is the default rather than an extra step you skip when you're busy.
Try it on your own keys
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