The goal is to get translations from GPT-4 in the way we are getting them from other MT services.

Yet different to other MT resources, we should find out, how it works to transfer additional information to GPT-4, what is the best way for doing so and if this leads to a better translation.

The following sources should be first evaluated and then if possible (what it should be) integrated for providing GPT-4 with additional information. If those sources are used, should be configurable.

• Send as many segments in one request as possible and tell GPT-4, that the structure of the segments need to be respected, but that the segments build a context. This will save costs, because GPT is payed by request and not by chars. And it will lead probably to a better translation.
• For each segment, where we have a 100% match or better, provide it to GPT and tell it, that we have already a translation for this and that it should use it as an inspiration for the other segments.
• For each segment send GPT the best X fuzzy matches in a structured way. We need to play around here, what makes sense. I would guess, it makes sense to send all fuzzy matches up to a certain percentage, e. g. 70%. And in addition send the best 3 fuzzy matches, if 3 do not exist above 69%.
• For each segment we mark in the source the found terminology and tell GPT how it should be translated (please see TextShuttle-Plug-in-Code, which also does this).
• With less prio than the previous: Find out, if we already can send images as context information to GPT-4. If yes: Implement it, that images are send as context.
• With less prio than the previous: Find out, if we can provide GPT-4 with the complete available TMs before the translation starts to train it. Or at least a certain number of matches.

More thoughts:

• send combined segments in a discernable structure to reduce costs
• send terminology markup and define the term's
• request alternatives for words
• Add frontend GUI to request segment phrasing changes e.g. regarding gender