# AI hallucination

> An AI hallucination is output from a language model that is fluent and confident but false, such as an invented citation, policy, figure or product detail.

- Page: https://methodcrew.com/glossary/ai-hallucination
- Last updated: 2026-10-28
- Publisher: [The Method Crew](https://methodcrew.com/)
- Also known as: confabulation

## How it works

A language model predicts likely text. It has no built-in check against a source of truth, so when it lacks the facts it can produce text that reads exactly like a correct answer. Hallucinations become more likely when a question falls outside what the model learned, when the prompt pushes for an answer at any cost, or when retrieval hands it the wrong passages.

The business risk is real. In [Moffatt v. Air Canada](https://decisions.civilresolutionbc.ca/crt/crtd/en/525448/1/document.do) (February 2024), a British Columbia tribunal held the airline responsible for wrong bereavement fare information its website chatbot gave a customer, and rejected the argument that the chatbot was responsible for its own statements.

## How businesses use it

Businesses manage hallucination with design choices, and the right mix depends on what a wrong answer costs:

- Grounding answers in your documents with [RAG](https://methodcrew.com/glossary/retrieval-augmented-generation) and requiring a citation for each claim
- Letting the assistant say it does not know when retrieval finds nothing relevant
- Validating structured output, such as checking an extracted invoice total against its line items
- Routing low-confidence or high-stakes answers to a person ([human-in-the-loop](https://methodcrew.com/glossary/human-in-the-loop))
- Running [evaluations](https://methodcrew.com/glossary/llm-evaluation) on a fixed test set before every prompt or model change

A draft marketing email can tolerate an occasional error that an editor catches. A quote, a dosage instruction or a refund policy cannot.

## Where it goes wrong

Teams assume RAG ends hallucination. It reduces it, but a model can still misread a correct passage, blend 2 sources into one wrong answer, or fill a gap the documents do not cover. Others rely on prompt lines such as "do not make things up", which help a little and guarantee nothing.

The other failure is not measuring after launch. Without monitoring and a weekly sample of answers reviewed by someone who knows the subject, errors surface as customer complaints. [AI ops monitoring](https://methodcrew.com/services/ai-ops-monitoring) covers that work once a system is live.

## Example (hypothetical)

Hypothetical: a 40-person HR consultancy's policy assistant is asked about parental leave in a state its handbook does not cover. It replies that the handbook has no answer for that state and offers to send the question to an advisor, with the conversation attached.

## Related terms

- [Retrieval-augmented generation (RAG)](https://methodcrew.com/glossary/retrieval-augmented-generation): Retrieval-augmented generation is a method where a system first searches your own documents for relevant passages, then gives them to a language model to answer from.
- [AI guardrails](https://methodcrew.com/glossary/ai-guardrails): AI guardrails are checks placed around a language model that block, rewrite or escalate inputs and outputs that break your rules on safety, accuracy, privacy or scope.
- [LLM evaluation (evals)](https://methodcrew.com/glossary/llm-evaluation): LLM evaluation scores a language model system's outputs against a fixed set of real test cases, so you can tell whether a change made it better or worse.
- [Human-in-the-loop (HITL)](https://methodcrew.com/glossary/human-in-the-loop): Human-in-the-loop is a workflow design where a person reviews, corrects or approves an AI system's output at set points before it reaches a customer or a system of record.
- [Large language model (LLM)](https://methodcrew.com/glossary/large-language-model): A large language model is a neural network trained on huge volumes of text to predict the next token, which lets it write, summarize, classify and answer questions.

## Services where this matters

- [AI ops monitoring and improvement](https://methodcrew.com/services/ai-ops-monitoring): Quality checks, cost control and model updates for live AI systems. $1,500 to $5,000 per month.
- [RAG and knowledge systems](https://methodcrew.com/services/rag-knowledge-systems): Your documents turned into an assistant that cites its sources. $15,000 to $75,000 per project.
- [Custom AI agents and copilots](https://methodcrew.com/services/custom-ai-agents): Agents that look things up in your CRM or helpdesk and take the next step, with approvals. $15,000 to $90,000 per project.

## Articles that use this term

- [AI acceptable use policy template for employees](https://methodcrew.com/insights/ai-acceptable-use-policy-template)

Full glossary: https://methodcrew.com/glossary