Teach AI your Corporate Language with Terminology

Inconsistent tone and AI hallucinations while creating the content for your company? Your LLM lacks the guardrails to stay on track. With Terminology-Augmented Generation (TAG), you feed your AI high-quality termbase data for more accurate, consistent, and cost-efficient content.

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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.

Your benefits with TAG

Clear, consistent texts, even across different departments

Terminology is always up to date thanks to real-time access

Fewer errors due to incorrect terms being used

Faster results, less post-editing

Efficient context instead of complex prompts

Scale content faster and still maintain consistent wording

Reduced token consumption (around 90%) and therefore lower costs

See How TAG Changes the Game.

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FAQs about TAG

What is the difference between TAG and "normal AI"?

With TAG, the AI is also given structured terminology as a form of guard rail, making its output more consistent and reliable.

What is the difference between TAG and RAG?

Retrieval-augmented generation (RAG) is good for finding additional content. Unfortunately for terminology it is often too imprecise: it tends to deliver "similar" or too many hits, which cost tokens and processing time, and can even reduce the quality of the output. TAG is a terminology-optimized form of RAG and instead delivers the correct term from your terminology, which usually makes it faster and cheaper.

What can TAG be used for in a commercial environment?

TAG has a broad range of possible uses. It can be used to produce terminologically and factually correct content and translations, perform terminology verification checks, or to extract definitions and context from the termbase. It can also be used with chatbots that recognize jargon and use the correct company terminology. Of course there are many more potential applications too.

What does this mean for terminology management?

A termbase becomes a strategic asset. The better the data quality (e.g., definitions, status, examples), the better and more consistent the AI output with TAG will be.

Must I have a termbase to use TAG?

A terminology data source is required for TAG. The fastest way to get started is with a termbase, but you could also take an existing glossary and expand it into a fully-fledged termbase with expert help from Kaleidoscope. Automatic terminology extraction with TermCatch can make sure you get all the essentials covered.

Who takes care of the content in the termbase?

Mostly the terminology or language team. They define the rules and content, such as preferred terms, definitions, and so on.

Why should the language department be involved with AI at all?

With TAG, terminology becomes the foundation on which the AI content is generated. This allows the language department to actively shape standards, ensure quality, and have an early say in AI projects.

Does it take long to integrate TAG?

Usually not. TAG can be integrated in a number of ways, but the easiest is with the Quickterm MCP Server in the AI Pro module: IT simply connects to the MCP server via URL and API token, while the terminology or language team handles the actual configuration.

Teach your AI to speak with your voice

Book a free call with our terminology experts and find out how TAG can bring your company to the next level!

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