Terminology-Augmented Generation

Why generative AI needs support

Generative AI (GenAI) is fast, but often not reliable. It is typically associated with two major weaknesses: hallucinations (where the AI invents details or facts that aren't true) and inconsistency (where different terms are used for the same thing and brand wording is not adhered to).

This means in practice:

• More corrections and internal approval loops needed

• Inaccurate statements regarding product and technical content

• Higher token costs because precisely defined context information cannot be sent with the prompts

• Additional effort due to imprecise retrieval (classic RAG approaches deliver "similar" instead of exactly matching hits)

Simply put, without clear terminology as a guide, AI-generated corporate content usually costs more in rework than it saves in initial labor. That is why we have developed terminology-augmented generation (TAG).

TAG – the next generation game changer

What is terminology-augmented generation (TAG)?


With TAG, the LLM has controlled access to your terminology when producing content and responding to queries. It can therefore use the correct terms in its output by evaluating the concepts, definitions, synonyms, and relationships in the termbase. In other words, it can learn to speak your company's language. 

Easy to connect

Quickterm provides an MCP server (Model Context Protocol) for efficiently connecting terminology databases to AI models in three simple steps: 

IT establishes the connection: Enter the URL and API token and your AI application is connected to the MCP server.

Terminology/language team takes over: Your experts determine what should happen, e.g., look up translations, provide definitions, find preferred terms, recognize synonyms or variants, or use related concepts as defined by concept maps.

AI uses terminology automatically: Whether for a chatbot, internal AI assistant, content generation tool or translation: the AI accesses the right concepts at the right moment.