The primary trade-off comes down to absolute terminology enforcement across large glossaries.
Conventional MT engines process text strictly segment-by-segment. While this creates the disjointed and erratic style described above, it does allow the engine to rigidly force specific glossary terms line by line.
Because the GAIT workflow operates in broad text blocks and continuous context windows to achieve superior fluency and stylistic coherence, it handles information differently. While the project anchor prompt guides the AI beautifully, the AI cannot pay attention to hundreds of target terms simultaneously. If a glossary is too aggressive, overloading the prompt makes the AI less reliable; if it is too sparse, important terminology can be left out of the anchor prompt entirely.
This is the one area where conventional segment-by-segment MT has a slight mechanical advantage—though let’s be honest, most conventional MTPE processes do not actually follow best practices here either, even when they have the capability.
To partially offset this, the GAIT workflow can process fuzzy matches individually alongside their corresponding TM hits to more closely leverage existing resources. However, because this targeted intervention is time-consuming, it is applied sparingly rather than as a blanket terminology and style enforcement tool.
For large glossaries, while the human linguist will naturally monitor term adherence during the post-editing phase (and the GAIT-Augmented MT & MTPE workflow will have generally already done a good job), the most efficient and rigorous solution is a standard CAT-tool terminology QA check against a well-maintained termbase on the finalized files before delivery. (This QA check is outside the scope of the workflow described here, though I am actively developing an automated post-editing validation layer to address this challenge even further).
Ultimately, while this means the workflow is not a completely water-tight automated solution for large terminology databases, the leap in overall draft fluency and structural clarity far outweighs this specific technical limitation in most situations.
