I have a growing archive of recorded courses and lightning lessons. Inside those long recordings are shorter explanations that could be useful on their own. Finding them means deciding where an idea begins, where it ends and whether someone can understand it without watching everything that came before. A good passage in a six-hour course is not automatically a good two-minute clip.
This was one of the workflows I demonstrated in a recent Maven lightning lesson on Jev. I supplied timestamped transcript text, asked it to select an opening and a compatible closing, then assess the resulting excerpt. It proposed passages for me to review. The video still needs to be checked and cut, but part of the editorial judgement can now sit inside a repeatable workflow.
Jev, from TypeSafe, is built for these kinds of bounded judgements. You supply evidence and specific questions, and it returns structured answers your software can use. It has three question types: Choice selects from options you define; Score places the evidence on a rubric; Noul returns a probability that a statement is true. General-purpose models can also classify and return structured output. Jev focuses specifically on this decision layer.
I think models like Jev are worth exploring wherever we make similar decisions repeatedly. They give us another way to build around the expertise we already have.
You can watch my lightning lesson on Jev on Maven. Alongside the transcript example, I demonstrate support-ticket triage and an AI task router, and explain how to define questions, interpret the answers and evaluate whether the workflow actually works.