A generic large language model (LLM) generates answers based on statistical probabilities from its training knowledge. That sounds convincing but can be wrong in detail, because the model has no knowledge of specific RATIONAL products, recipes, or service processes.

A RAG system solves exactly this problem: with every query, it draws on the actual company data, including operating manuals, recipes, and service documentation. The language model formulates its answer exclusively based on these verified sources. The result is factually correct answers rather than plausible-sounding guesses.

In concrete terms: when a chef asks about the right cooking level for a steak or a technician about the right cleaning tablet, the AI assistant delivers an answer based on genuine RATIONAL product data.

At the same time, the system addresses the skilled labor shortage in technical service. Experiential knowledge that previously existed only in the minds of individual employees becomes digitally accessible: for customers directly and for new service staff as a knowledge resource. Less dependency on individual knowledge holders, faster onboarding, and a customer service that delivers quality even with a smaller team.