Can AI hallucinations be prevented?

Hallucination is not a defect you can patch — it is a structural property of how probabilistic models work. The goal is not elimination but containment: making hallucination observable, bounded, and safe to deploy.

Layer one: retrieval grounding. Before the model generates, retrieve relevant documents from a trusted source. The model answers 'based on the retrieved content' rather than 'from memory.' This is what RAG (retrieval-augmented generation) does.

Layer two: structured outputs. Constrain the model to output JSON with specific fields, or categories from a fixed list. Free-form text is where hallucinations live. Structured output is where they die.

Layer three: business-rule validation. After the model produces output, validate it against the rules your operation actually requires. Dosage within range? Amount within policy? Identifier exists in the database? Reject and retry if not.

Layer four: deterministic decision layer. For the actions that must be reliable — the final approval, the financial transaction, the regulatory filing — the decision happens in deterministic code, with the generative output as input rather than output.

SynthesisArc's PRISM implements all four layers by default. Claude Guard monitors and enforces the policies. The result is a system that uses generative capability where it adds value and deterministic logic where reliability is non-negotiable.