working on Meridian, my newsletter that aggregates insights from crypto  podcast, I leverage LLMs regularly as leverage. Sometimes to eliminate things that would otherwise be a pain in the neck to write.

The context is in the conversation, and is an easy task for a human. For examples sake, we can say that introductions occur within the first few minutes of a podcast and the enrichment can occur

The last issue of the newsletter covered 22 podcasts, that is a pretty heavy time investment for each issue. So I decided to see how far I could get with some of the easily accessible models

We sort of do this heuristic in our head of eliminating who the speaker isn't each time we listen to a new podcast. Since we can give the model a fair bit of context it should be able to knock it all down

I asked claude 3.7 Sonnet to write me a prompt for gpt-4o for the speaker assignment and now I have something I can pass a transcript to ,it samples, and provides predictions of which speakers are which

with that said, feel free to sign up to https://www.getmeridian.io/

transcribing podcast in generally a starting point, base transcriptions can identify that several people are talking but not who they are. So enrichment is needed