# Hallucination

**Source:** https://promtable.com/glossary/hallucination

> A hallucination is when a language model produces output that is factually wrong, fabricated, or unsupported, while sounding confident.

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A hallucination is when a language model produces output that is factually wrong, fabricated, or unsupported, while sounding confident.

Hallucination is the most discussed failure mode of LLMs. It happens because language models predict plausible next tokens, not verified facts — when ground-truth knowledge is missing or contradictory, the model fills the gap with statistically likely but false content. Common forms include invented citations, fake API signatures, wrong historical dates, and confident answers to questions outside the model's knowledge cutoff. Mitigations include retrieval-augmented generation (RAG), explicit uncertainty prompting ("If unsure say 'I don't know'."), output verification with a second pass, and lower temperature for factual queries.

## Common mistakes

- Assuming bigger models hallucinate less — they often hallucinate more confidently.
- Treating hallucination as a prompt-only problem when retrieval is the real fix.
- Not running factual evals before shipping.

## Related terms

- [rag](https://promtable.com/glossary/rag)
- [grounding](https://promtable.com/glossary/grounding)
- [temperature](https://promtable.com/glossary/temperature)
- [chain-of-thought](https://promtable.com/glossary/chain-of-thought)

## Sources

- [Ji et al. 2023 Hallucination Survey](https://arxiv.org/abs/2202.03629)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/hallucination
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