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Glossary · E-commerce

Hallucination

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E-commerce

A hallucination is content that an AI model makes up while generating its output and that was part of neither its input nor its training data.

Germany's Federal Office for Information Security (BSI) covers hallucinations in its study on generative AI models (in German). Many models invent content when they process inputs on current topics. Hallucinations are a major problem with large language models, because the outputs usually look credible, especially when they cite references that may themselves be entirely made up.

According to the BSI, giving a model a knowledge base through RAG is a common way to avoid hallucinations, and showing users the text excerpts behind an answer can mitigate the effects, though not eliminate them. Where the impact may be critical, outputs should be checked and, if required, edited by hand before further use.

Checks are built in from the start, because we assume an AI agent will get something wrong sooner or later. Take the knowledge platform we built for a pharmaceutical logistics company: the model behind its analysis software must not invent or guess, and where abbreviations, business context or technical details are unclear, it has to ask. The platform's interface, not the model, assigns values such as the next free case number. In one research pipeline, a second agent on a different model checks the work. A statement counts as a fact only once a script has found the supporting quotation word for word in the source.

Where the term appears

Sources

  1. Generative KI-Modelle: Chancen und Risiken für Industrie und Behörden, Version 2.0 vom 17.01.2025 (opens in a new tab)

    Bundesamt für Sicherheit in der Informationstechnik (BSI) accessed

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