How domain context improves transcription post-processing (Full Transcript)

[00:00:00] Speaker 1: Maybe on the point around just like post transcription, post processing, maybe give folks in the audience an idea of like what are you actually doing to like customize that output per domain? Are you like boosting certain terms? Are you just like running multiple LLM workflows? Like maybe just walk us through what that looks like.

[00:00:15] Speaker 2: Yeah, yeah. So we get the transcript out from assembly. I guess like in order to understand this, users who go into CodeLoop and they create a project, doing qualitative research you almost always have like a discussion guide that is basically underlying the interviews that you do and within those discussion guides there's a lot of context about like what is this research about? Who's involved? Like what are the key terms? What questions are we asking? What objectives are there? So there's a lot of really rich context in there that can support transcription. And so what we do is we take that context and we get out various bits of structure information like keywords that we can pass into assembly in the first instance but also secondarily like once the transcription is done, we effectively have an LLM pass that is going over the transcript and basically like an AI coding agent is making edits in the transcript so like passing in like the original string and then like the stringer wants to replace it with in order to like insert and edit at a specific point based on like various instructions around like phonetic like mistranscriptions or like words that maybe should be joined together or like all sorts of things. And in that way we're basically able to then correct the transcript and we've set up a number of evals to test that so we sort of like work backwards from a clean transcript and like insert like problems into it to like validate that that works quite well and so we built up an eval data set.