Hallucination
Halluzination
A fluent, confident answer that is wrong or made up. It is a property of how language models work, not a rare bug.
01In short
A fluent, confident answer that is wrong or made up. It is a property of how language models work, not a rare bug.
In productionReduce it with grounding, catch it with evals, and design the process so a human sees what matters.
02Video
slot · videoHallucination in 90 secondsadd: GUIDES['hallucination'].video
03Guide
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A step-by-step guide for “Hallucination” goes here. Suggested outline:
- What it is — in one paragraph
- Why it matters in production
- How to do it — 3 to 7 steps
- Pitfalls we see in the field
04Checklist
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Four to eight things a team can tick before go-live.
05FAQ
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The three questions clients actually ask about “Hallucination”.
06Related terms
Data & retrievalGroundingTying a model’s answer to specific sources it was given, ideally with citations. The main defence against hallucination in business use.Data & retrievalRAGBefore answering, the system searches your documents and hands the relevant passages to the model. Answers get grounded in your data without retraining.EvaluationEvalsSystematic tests of an AI system’s outputs against expected results, run automatically after every change.
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