Hallucination
A hallucination is when a language model produces confident, fluent text that is factually wrong or made up — a citation that doesn't exist, a fabricated figure, an invented API. It happens because the model predicts plausible text, not verified truth, and it is the central reliability problem in AI products.
Models generate the most likely next words, and a confident-sounding wrong answer is often statistically as plausible as the right one. So hallucinations look exactly like correct answers — fluent, assured, sometimes with fake sources attached. This is dangerous precisely because there is no obvious tell; the model is not lying, it has no notion of truth to lie about.
When building a product, you don't eliminate hallucination, you engineer around it. Ground the model in real data with RAG so it answers from retrieved evidence and can cite sources, constrain it to verifiable outputs, add guardrails that catch impossible answers, and design the UI so users can check rather than blindly trust. And you measure it with evals on real cases. We design on the assumption that the model can be confidently wrong, so the product stays trustworthy even when a given answer isn't — that honesty is the whole point of doing this in-house and carefully.
// faq
Frequently asked questions
- How do I stop my AI from hallucinating?
- You reduce it rather than eliminate it. Ground answers in your real data with RAG so the model responds from retrieved evidence and cites sources, constrain outputs to checkable forms, add guardrails for impossible results, and keep a human in the loop for high-stakes answers. Then measure with evals on real cases. The aim is a product that stays trustworthy even when one answer is wrong.
- Does RAG eliminate hallucination?
- It reduces it significantly but doesn't eliminate it. RAG grounds the model in retrieved facts, so there's much less room to invent — but the model can still misread a passage or answer beyond what was retrieved. You still need guardrails, source citations users can check, and evals. RAG is the biggest single lever, not a complete cure.