Hallucination control is the enforcement of factual boundaries on AI outputs so that unsupported, fabricated, or non-compliant content is restricted before it reaches end users.
Large language models generate plausible but incorrect content as a fundamental property of how they work. Without governance, hallucinated outputs — fabricated facts, invented citations, non-existent policies — reach production and erode trust with customers, regulators, and internal stakeholders.
Hallucination control applies customer-defined policies to evaluate each governed AI output before delivery. Content that violates factual boundaries receives an explicit decision — restricted or escalated — and a reviewable record is created for each governed interaction.
Without hallucination control, organizations rely on end users to catch fabricated content. With it, each governed output receives an explicit decision against customer-defined policy before delivery, and that decision is recorded. It reduces exposure; it does not guarantee that delivered content is factually correct.
Prompt engineering tries to guide model behavior through input design. Hallucination control enforces factual boundaries on outputs regardless of prompt quality.
Fine-tuning adjusts model weights to change behavior. Hallucination control governs outputs without modifying the underlying model.
Fact-checking reviews content after delivery. Hallucination control evaluates outputs before they reach end users, producing explicit decisions in real time.