Original Summary

I'm a solo dev in Portugal building MicroGrind, a study app for university students. Most of the code is written with AI coding agents in Cursor, and a lot of my time goes into reviewing, testing and steering them. Stack is Expo/React Native with Supabase, web and mobile, still in development. The idea: instead of throwing your PDF at a model and getting 50 random flashcards back, the app first builds a "map of the subject". Topics, concepts, definitions and how they relate, with each piece tied to the parts of your document that support it. Only after that does it generate short lessons, flashcards and questions, and it tracks what you know concept by concept to pick your next 5 to 25 minute session. The rule I set early on: being correct isn't enough, it has to be supported by the student's document. If your law notes only list three types of authorship without explaining them, the app shouldn't invent a lesson about each one, even if what it writes would be true. Honestly, it's not there yet. I ran an internal evaluation on real documents and the maps still had claims with no source support behind them, and some lessons came out too thin to be useful. Models love to "help" by filling gaps with general knowledge, which is exactly what I don't want here. So right now I'm working on the pipeline, not features: better extraction, tighter links between concepts and the passages behind them, and checks that flag anything unsupported. For anyone who has built on top of LLMs: how do you keep generated content faithful to a specific source without it becoming so cautious it's useless?   submitted by   /u/LopsidedName2437 [link]   [comments]

  • 情报分类：技术学习与提效
  • 分类依据：内容涉及技术、AI、软件工具或工程实践
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/9/27 00:56:22