Make AI More Reliable with the AI Pro Module

Using the power of terminology-augmented generation (TAG), the AI Pro module is designed to help large language models (LLMs) adhere to an organization's corporate language, reducing issues like inconsistencies and hallucinations.

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Fine-Tune Your AI with the Power of the AI Pro Module

With the AI Pro module, you can leverage the power of terminology-augmented generation (TAG) and inject your organization's terminology directly into AI applications. The AI Pro module offers many advantages, including:

Highly effective model fine-tuning

More precise context retrieval (vs other methods)

Hallucination risk reduction

Flexible output formats

Fewer tokens required (vs other methods)

Real-time access to current data

Providing an effective interface between terminologists, AI, and content developers, the AI Pro module ensures that the steps and tools used along the path to publication or translation are more transparent, reliable, and streamlined.

The AI Pro Module Offers You a Cutting-Edge Way to Leverage Terminology for AI

Unlike endless prompt experimentation or context-detached data annotation, the AI Pro module uses terminology-augmented generation (TAG) to fine-tune models more precisely while using fewer tokens. It is also faster and more flexible because:

AI Pro can also be used by other systems to retrieve terminology

AI Pro helps infuse terminology into all AI applications

AI Pro is a less compute-intensive fine-tuning method

Flexible output formats

AI Pro applies terminology consistently and contextually

AI Pro reduces the need for multiple reviews

The AI Pro Module Offers Solid Technical Foundations

For AI engineers, the AI Pro module solves the challenge of making models adhere to a corporate voice and brand language assets, at the same time providing more context thanks to:

Terminology-augmented generation (TAG): Using your own valid terminology is one of the best ways to fine-tune a model with context-valid data.

A structured data model: Termbases use a precise and structured data model, which makes it easy to retrieve relevant information.

Precise retrieval: The AI Pro module uses classic terminology methods like search types and filters for precise and deterministic data retrieval.

Superior connectivity: With the AI Pro module, AI engineers can call the API directly or embed the MCP server into their apps for an agentic AI workflow.

Flexible output: Terminologists can configure exactly what gets returned and in what format, so that LLMs can best "unterstand" the instructions for the give use case.

Real-time access via API: The AI Pro module's API access makes it possible to search termbases faster and in real-time vs. searching pre-embedded data.

All these properties are enabled by an API endpoint that linguists can configure, making it possible to loop the model context protocol (MCP) embedding process to the translation and localization steps.

Find out how you can enhance your AI implementations using your own terminology with the AI Pro module.

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In a Nutshell: Frequently Asked Questions

How can I infuse terminology into AI using the AI Pro Module?

You can use the AI Pro module by calling an API endpoint or embedding our model context protocol (MCP) server into your applications. This approach adds context via terminology-augmented generation (TAG) to reduce hallucinations and ensures terminology adherence.

How can I make my LLM speak my organization's language with the AI Pro module?

By infusing terminology into the large language model (LLM) via the retrieval endpoint. The linguist and AI engineer can work together to 1) configure the profiles that define the search type, filters, output format, etc., and 2) call the endpoint via API or more easily by embedding our MCP server. This approach provides the LLM with the necessary context to adhere to your organization's voice and specific terms.

What problem does the AI Pro module solve better than other methods?

The AI Pro module addresses the core issue of AI model hallucination or failure to adhere to an organization's language and specialized terminology by relying on terminology-augmented generation (TAG) and a precise sequence of actions. Unlike other methods like prompt engineering or retrieval-augmented generation (RAG), the AI Pro module offers a deterministic, fast, and precise way to effectively inject pre-configured, context-aware terminology (managed with Quickterm) into your AI applications.

Retrieval-augmented Generation (RAG), on the other hand, is a technique that grounds a large language model (LLM) by retrieving relevant, external information from a trusted data source and adding it to the LLM's prompt before generating a more accurate and contextualized response. Terminology-augmented generation (TAG), which the AI Pro module uses, is more precise than RAG because returns exactly the information required in exactly the format that can be best understood by the LLM for a given use case.

Can the AI Pro module be used to improve non-AI applications?

Yes. While the AI Pro module is primarily designed to augment and enhance the accuracy of large language models (LLMs), the same retrieval endpoint might help with other use cases that require retrieving terminology for external systems, such as quality checking tools or other downstream applications.

What data formats can the AI Pro module output from a termbase?

The AI Pro module is highly flexible and can produce terminology in lightly structured formats that work well with large language models (LLMs). These outputs include formats like markdown, YAML, JSON, and prose, allowing for easy integration into various AI and data workflows..

How can an AI engineer integrate the AI Pro module into their workflow?

AI engineers can either directly call the API endpoint configured by the linguists or embed the model context protocol (MCP) server into their large language model (LLM) or agentic workflows, allowing the terminology data to be accessed in real-time as the AI processes information.

Manage Your Terminology with Quickterm

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