How can you prevent AI hallucinations in business?

Build checks around the information a system uses, the claims it produces and the actions it can take. Define when it must stop, seek clarification or route an exception to a person.

Updated October 11, 2026

Where should prevention start?

Start by defining the task and its authoritative records. For example, an internal expenses assistant needs the policy that applies to the employee, expense date and location. This is a hypothetical design example: choose these conditions deliberately rather than leaving the model to infer them.

Then specify what the output is allowed to claim and which actions it can initiate. Keep the source, interpretation, validation result and action outcome inspectable.

Which controls address which problems?

The following is our recommended review sequence. The controls should be tested together on the intended workflow.

• Retrieval from approved records. Question to test: Did the system find the relevant passage and version?. Evidence to retain: Source identifier and retrieved passage.
• Claim verification. Question to test: Does that passage support the particular statement?. Evidence to retain: Claim-to-passage mapping and review result.
• Explicit calculation and policy rules. Question to test: Do the checked inputs satisfy the rule?. Evidence to retain: Inputs, rule version and calculation result.
• Abstention and clarification. Question to test: What happens when evidence is absent or conflicting?. Evidence to retain: Unresolved question and reason for stopping.
• Human exception handling. Question to test: Can a person inspect and resolve the issue?. Evidence to retain: Decision, identity, time and approved scope.
• Action confirmation. Question to test: Did the destination system accept the authorized action?. Evidence to retain: Request, response and resulting state.

Why use more than retrieval?

The legal-tool reliability study reported hallucinations in the retrieval-based products it tested. That is a reason to test the generated answer itself, not to infer reliability from retrieval being present. The abstract does not establish today’s performance of those tools. Read the study.

How can formal policy checks help?

AWS documents Automated Reasoning checks that validate responses against defined policies and return structured findings. It also states that these checks do not provide prompt-injection protection and validate the supplied content as-is. Those are specific documented capabilities and limits, not a comparison of vendors. Read the documentation.

Our implementation recommendation is to connect each finding to a handling rule: accept a validated answer within scope, ask a clarifying question, or withhold the action and escalate. Also review the policy itself; consistency with a policy is the property this check addresses.

What should happen when the system cannot proceed?

Use an explicit unresolved state. In a hypothetical reimbursement process, a missing policy version should produce a request for clarification, not an invented limit. A failed payment confirmation should remain unresolved until the payment system establishes its status. Do not turn an exception into a success message merely to finish the workflow.

Agree who can resolve an exception and how a resolution may affect later runs. Keep that decision visible in the record.

How do you know the controls work?

Test ordinary and adversarial examples, missing evidence, contradictory records and unsupported requests. Assess the combined outcome: the answer delivered, the action taken and the exception handling. Use the measurement guide to separate errors from abstentions.

Read what a hallucination-free claim should cover and the companion guide to testing determinism.

Reading scope

• arXiv: Abstract and metadata read; findings refer to the tools evaluated in this study.
• Amazon Web Services: Introduction and beginning of limitations read; cited for policy validation and structured feedback, not a universal accuracy guarantee. Accessed 2026-10-11.