Definition: An AI hallucination is an output from a language model that sounds confident and plausible but is false, unsupported by its sources, or invented, such as a made-up citation or number.
A large language model predicts likely text; it does not look facts up unless a system gives it sources. When the answer is missing from its context, ambiguous or outside its training data, it can still produce fluent text that fills the gap.
The main levers are grounding answers in approved data with retrieval-augmented generation, asking the model to cite sources and to say when it does not know, constraining outputs with schemas, adding guardrails that check claims against retrieved text, and routing low-confidence cases to a person.
Hallucinations cannot be removed entirely, so teams measure them. An evaluation set of real questions with known answers shows how often the system is wrong and whether a change made it better or worse.
In customer support, finance, legal or healthcare workflows, a confident wrong answer can cost more than no answer. Measuring and limiting hallucinations is usually what decides whether a pilot is ready for production.
Ryz LLM evaluation engineers and RAG engineers build grounding and evaluation into your AI systems, working on your team.
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