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Glossary · AI

AI hallucination

An AI hallucination is output from a language model that sounds confident but is false, made up or not supported by its sources, such as an invented citation, figure or policy.

In one line

An AI hallucination is output from a language model that sounds confident but is false, made up or not supported by its sources, such as an invented citation, figure or policy.

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Why hallucinations matter

Hallucinations are hard to spot because they read just like correct answers. In customer support, finance or legal work, one confident error can cause real harm and damage trust in the whole product.

How hallucinations happen

Language models generate likely-sounding text, not verified facts. When the answer is missing from their context, they may fill the gap with something plausible.

Vague prompts, poor retrieval and questions outside the model's knowledge all raise the risk.

Common mistakes with hallucinations

Promising users that the system never makes things up. Hiding sources, so people cannot check. Having no "I don't know" path, which pushes the model to guess.

How Gr8r approaches hallucinations

We reduce them with grounded retrieval, clear instructions and checks in code. Then we design for the ones that remain: showing sources, flagging low confidence and routing risky outputs to a person.

Example

A policy assistant is told to answer only from retrieved documents. When nothing relevant is found, it says so and offers to pass the question to the HR team instead of guessing.

FAQ

Questions

  • Can hallucinations be eliminated?

    No current method removes them entirely. They can be reduced and managed with grounding, checks, evaluation and good interface design.

  • Does a bigger model hallucinate less?

    Often, but not always, and never to zero. Testing on your own tasks is the only reliable way to know.

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