Hallucination

Grounding is the practice of constraining an AI agent’s answers to approved knowledge sources and live system data rather than to the model’s own training. Grounding is the main defence against hallucination, and it is what makes an AI answer auditable back to a specific document or record.

Retrieval-augmented generation grounds answers by retrieving an organisation’s own documents at the moment of answering and passing them to the model as context. It keeps responses current without retraining, and it makes each answer traceable to the specific source the system actually consulted.

A fallback is the safe response an AI agent gives when its confidence is too low to act, typically an offer to hand the conversation to a human. Well-designed fallbacks fail early rather than guessing, because a confidently wrong answer costs far more than an admitted gap.

An evaluation set is a fixed sample of real conversations used to score an AI agent’s quality before and after changes. Holding the sample constant is what makes results comparable, so evaluation sets are versioned and refreshed only deliberately when the underlying contact mix genuinely shifts.