Hallucinations

Fluent, confident output that is false or unsupported — invented facts, figures, quotes or sources — presented as if it were true.

Last updated: November 9, 2026 · Markdown version

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How to apply it in prompts

Use this concept to reduce ambiguity and improve consistency. If you need strict formatting, combine it with a JSON guide and a per-model template.

Strict JSON Models Packs

What it means

Definition: a language model generates plausible text, not verified facts. When it lacks the information, it can still produce a convincing answer that happens to be wrong.
Why it matters: the output reads well, so errors are easy to miss. The risk is highest for specific details — numbers, dates, quotations, citations, API names.
How to reduce it: give the grounding material in the prompt and say to answer only from it; allow 'I don't know' or 'not enough information'; ask it to mark what it is unsure of; for current facts use a tool that searches and returns sources.
Common mistake: trusting a source the model produced without opening it. Prompting lowers the rate of invented content but does not eliminate it — verify anything that matters.

Copy-paste example

Answer using only the context below.
If the answer is not in the context, reply exactly: "Not enough information".
For every claim, quote the sentence from the context that supports it.

Context:
"""[paste]"""

Question: [question]

Opens the analyzer with the prompt prefilled.

Open in Promptea

FAQ

Can a prompt eliminate hallucinations?

No. Grounding the answer in supplied text, allowing 'I don't know' and requiring supporting quotes lower the rate a lot, but you still need to check facts that matter.

Are some tasks more prone to it?

Yes: questions about obscure or recent facts, requests for citations, exact figures and long chains of reasoning. Tasks that transform text you supply (summarize, extract, rewrite) are safer.