# Hallucination

> A hallucination is a confident-sounding output from a generative AI model that is not grounded in any source and is factually wrong.

Category: Production
Source: https://impetora.com/glossary/hallucination
Part of: Impetora AI consulting glossary (https://impetora.com/glossary)

## What is Hallucination?

Hallucinations arise because language models generate the most plausible continuation, not the most truthful one. Common causes include missing context, leading prompts, training-data gaps, and over-extrapolation. Mitigations include retrieval-augmented generation, grounding requirements, citation enforcement, schema-constrained outputs, retrieval-then-validate patterns, and human review on high-stakes paths. Hallucinations cannot be fully eliminated; they can be substantially reduced and made detectable.

## How does Hallucination apply to enterprise AI?

In regulated workflows like legal, healthcare, and financial advice, an unmitigated hallucination is a compliance event, not just a quality issue. Enterprise AI systems must be designed assuming the model will sometimes be wrong.

## Related terms

- [Large Language Model](https://impetora.com/glossary/large-language-model) - A Large Language Model (LLM) is a foundation model trained on text to predict the next token, capable of generating, summarising, and reasoning over natural language.
- [RAG (Retrieval-Augmented Generation)](https://impetora.com/glossary/rag) - Retrieval-Augmented Generation (RAG) is an architecture pattern that grounds a language model's output in retrieved source documents rather than relying on the model's parametric memory alone.
- [Guardrails](https://impetora.com/glossary/guardrails) - Guardrails are runtime checks placed around an AI system to constrain inputs, outputs, and tool calls within safety, compliance, and business policy.

## External references

- [Ji et al., Survey of Hallucination in NLG](https://arxiv.org/abs/2202.03629)

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