Hallucination
When a model states something false with full confidence — the failure mode grounding, citations, and evals exist to contain.
Production AI is a system: context, tools, permissions, traces, evals, and feedback loops around the model.
What it is
A hallucination is a fluent, confident, wrong answer: an invented policy, a citation to a document that doesn't exist, a number that was never in the source. It happens because language models generate plausible text; plausibility and truth usually overlap, and a hallucination is where they don't.
Why it matters
For enterprise AI this is the failure mode that costs trust fastest. One invented refund policy told to a customer outweighs a hundred correct answers. The goal in production is not a model that never errs — none exists — but a system where wrong answers are rare, detectable, and cheap to correct.
How we contain it
Grounding: answers draw from retrieved documents, not the model's memory. Citations: every claim links to its source, and unverifiable citations are flagged rather than shown. Refusal: the assistant says 'I don't know' when retrieval comes back thin. Evals: groundedness is scored continuously, so regressions surface as numbers instead of incidents.
Related resources
The pattern where an AI agent retrieves relevant context from your data before generating an answer — instead of relying only on what the model learned during training.
Your AI shows exactly which document each statement came from — and says so honestly when a statement has no source — so people can check its work instead of just trusting it.
The safety checks and policy enforcements that sit around an AI agent's inputs and outputs — content filters, scope enforcers, PII redactors, refusal patterns, and tool-call validators.
Short for 'evaluations' — the test cases and harness that measure whether an AI workflow is working, before and after every change.