Hallucinations

Hallucinations come from how Large Language Models work. An LLM is trained to generate plausible next words, not to make true statements. When it lacks concrete knowledge, it still produces an answer that sounds convincing but is factually wrong. Examples range from invented book titles to wrong numbers to fabricated legal provisions.

In product data applications, hallucinations are especially risky. A model that generates a product description with wrong technical data or invented features can cause real damage: returns, customer complaints, regulatory issues. Hallucinations have to be actively prevented in production AI pipelines.

Pimcore reduces hallucinations through architectural design. AI functions work on the Data Spine as their factual foundation, instead of generating from model memory. The Agent SDK uses retrieval augmented generation: before the model answers, it pulls relevant data from PIM and MDM and uses it as context. Workflow steps with human approval catch the residual risk.