What a hallucination is
A language model generates text that is statistically likely given its input, not text it has verified. Sometimes that produces a fluent statement that is false: an invented policy, a made-up citation, a plausible but wrong figure. The model does not know it is wrong, and its tone gives no warning.
In casual use that is an inconvenience. In a business workflow, a hallucinated refund policy or contract date can cost money and trust.
Where hallucinations come from
- The answer is not in the input. The model fills the gap rather than saying so.
- The question is outside its knowledge, or the information is out of date.
- The instructions are ambiguous. The model guesses what you meant.
- Retrieved context is poor. Wrong or irrelevant documents lead to wrong answers.
- The task needs precision such as arithmetic or exact quotes, which models do unreliably.
Reduce the risk by design
| Technique | How it helps |
|---|---|
| Ground answers in your documents | The model answers from supplied text instead of memory |
| Require citations | Every claim points to a source a person can check |
| Allow “I don’t know” | Removes pressure to invent an answer |
| Constrain outputs | Fixed categories and fields leave less room to improvise |
| Use code for facts and math | Lookups and calculations come from systems, not the model |
| Validate against systems of record | Reject outputs that name a customer, order or product that does not exist |
| Add a verification step | A second pass checks the answer against the sources |
| Keep tasks narrow | A focused task is easier to get right than an open-ended one |
| Review high-stakes outputs | A person confirms before action |
Grounding in practice
For question-answering, retrieve the relevant passages from your approved documents, give them to the model and instruct it to answer only from them, citing the passage. If nothing relevant is found, the system should say so rather than answer from general knowledge. Test with questions that have no answer in your documents to confirm it declines.
Validate before you act
Treat outputs as claims to check:
- Does the order number exist, and does it belong to this customer?
- Do line items add up to the total?
- Is the date plausible?
- Is the policy cited actually in the current handbook?
Validation rules catch a surprising number of hallucinations before they reach anyone.
Test for hallucinations deliberately
- Include unanswerable questions in your test set.
- Include near-miss questions, where the right answer is subtly different from a common one.
- Test with missing fields and see whether the system guesses.
- Review samples of live output weekly.
- Track how often people correct factual errors, by category.
Set expectations with users
Tell users when answers come from AI, show sources and make it easy to report a mistake. For customer-facing tools, provide a route to a person. Do not present AI answers on legal, medical or financial matters as professional advice.
What does not work
- Telling the model “do not hallucinate.” Instructions help only a little without grounding and checks.
- Relying on a more powerful model alone. Larger models hallucinate less on many tasks, but not never.
- Assuming a confident tone indicates correctness.
FAQ
What causes AI hallucinations?
Models generate likely text rather than verified facts. They hallucinate when the answer is missing from the input, the question is ambiguous, the retrieved context is poor or the task needs exact precision.
Can hallucinations be eliminated completely?
No. They can be reduced substantially with grounding, constraints, validation and review, but you should design workflows assuming that errors will occasionally occur.
Does RAG stop hallucinations?
It reduces them by giving the model relevant source text, but it does not remove them. Poor retrieval, ambiguous documents or misreading can still cause errors, so add citations and testing.
How do I handle hallucinations in customer-facing chatbots?
Ground answers in approved content, require citations, let the bot decline when unsure, add easy escalation to a person and review conversations regularly.
Sources and further guidance
Platform features and requirements change. Check the linked primary documentation for your implementation.
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