How to reduce AI hallucinations

A hallucination is when an AI model produces fluent, convincing but false or made-up information: citing a source that does not exist, a wrong number, a product feature that is not real. It is hard to eliminate entirely but can be reduced significantly with the right design.

Why it happens

Language models generate answers by predicting likely text; they tend to produce a plausible answer instead of saying "I don't know". The risk rises when information is missing from or outdated in training data, or when questions are ambiguous; see how LLMs work.

Product risks

• Giving customers wrong price, refund or contract information
• Harmful errors in law, health or finance
• Damaged brand trust

Ways to reduce it

Grounding

Base answers on trusted documents and ask for citations; see our RAG guide.

Prompting

• "Use only the given context; if the information is missing, say you don't know"
• Ask the model to find relevant quotes before answering
• Allow it to ask for clarification on ambiguous questions

See our prompt engineering guide.

Structured outputs and tools

Have calculators, databases or APIs handle calculations, dates and prices instead of letting the model guess; see AI agents.

Verification

• Automatically compare key facts with sources
• Human approval for high-risk answers
• Consistency checks with a second model

Narrowing the task

Well-defined tasks hallucinate less than broad, open-ended ones.

Interface design

• Show sources and quotes.
• Present AI output as an editable draft.
• State uncertainty clearly.
• Make it easy to report errors.

Measurement

Measure accuracy and groundedness regularly in your eval set; see our evaluation guide.

Conclusion

Hallucinations are the main trust problem of AI products. Grounding, good task design and a transparent interface are the foundations of a reliable product. For customer communication, see AI in customer service.