Category
AI / Trados Studio / Integration & UX

Sub-Title
Next-Gen AI Integration: Human-in-the-Loop Real-Time Terminology & Logic Governance

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Description / Idea Overview
While the integration of LLMs and RAG (Retrieval-Augmented Generation) into Trados is a welcomed evolution, current API-level connections encounter a fundamental architectural limitation when handling complex enterprise documentation: the loss of In-Document Dynamic Consistency.

RAG excels at aligning text with past, static databases (TMs and Glossaries). However, when translating unreleased software specifications or novel UI frameworks, new terms and dynamic logic emerge organically on page 1 that must be enforced through page 100.

Current LLM batches cannot auto-correct these emerging shifts in real time. This is precisely where Trados’ greatest strength lies—its human-centric "cockpit" interface featuring real-time segment propagation, dynamic termbase updates, and precise segment-level control.

Rather than treating AI as a background bulk-generator, Trados has a unique opportunity to lead the market by creating a truly interactive Human-in-the-Loop AI governance interface.

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Key Requirements & Proposed Enhancements

1. Dynamic Real-Time Term Propagation for LLM Prompts
When a linguist establishes or modifies a new translation choice in Segment A, the underlying AI context window/RAG index should dynamically update in real time, forcing subsequent AI-suggested segments to strictly comply with the newly established in-document logic.

2. Interactive AI Steering UI ("Cockpit" Control)
Instead of a simple "Generate/Accept" button, introduce an intermediate control layer where linguists can flag emerging terminology on the fly, instructing the AI to re-align remaining unconfirmed segments across the entire document.

3. Latency-Optimized Dynamic Indexing
Develop a lightweight, local/session-based context buffer for the active document that interfaces between the active segment and the LLM API, ensuring real-time responsiveness without the heavy lag of full database re-indexing.

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Why This Matters to RWS & Trados Users

General-purpose AI developers (OpenAI, Anthropic, Google) focus purely on autonomous generation and lack the deep domain expertise required for localization UX.

If RWS can bridge the gap between raw LLM generation power and Trados’ decade-proven real-time governance UI, Trados will solidify its position not just as a traditional CAT tool, but as the indispensable Enterprise Contextual Quality Assurance (CQA) Platform for the AI era.