Using Jev to batch-check working-memory load in course content
The author ran working-memory checks on course content with Jev, which would cost about $250,000 with Astra at the same scale but only about $2,000 with Jev.
Jev has accelerated the timeline for personalized education by years. The biggest bottleneck in building personalized education with AI is validating the quality of the material it generates. For the courses I’m building, generating lessons is only ~5% of the content cost. The other ~95% is validation and improvement. One of the biggest costs is checking whether a lesson gives a student too much information to process at once, overloading their working memory. Running that analysis across all of our content with frontier models gets incredibly expensive. At our current scale, even if Astra used half as many tokens as Jev, it would cost us ~$250,000. With Jev: ~$2,000. I built the tool we’re using for the working-memory check and made it available here: https://github.com/AustinAWay/Working-Memory-Jev