Original Summary

Side project, nights and weekends, no funding. My co-founder Dayna has 25 years of counseling families through college. I have a product marketing background but I don't write code; AI did all of that. The product is Aiko, a free AI college advisor that shows where a student fits and what each school would really cost after aid. About 2,500 four-year US colleges. The stack, for the curious Data: free federal sources (IPEDS, College Scorecard, plus campus-safety and athletics data) loaded into Postgres Backend: Python (FastAPI) with the Claude API for the advisor Frontend: Expo, one codebase for web A bug that pissed me off The model had access to the raw cost table, and it quoted a family earning $80K the school-wide average price. Every number was real data read off the wrong row. It sounded confident and was off by tens of thousands of dollars over four years. The fix wasn't a better prompt. It was never handing the model raw inputs for something my code already computes: I hand it the answer, labeled with what it rests on. If you build on an LLM, that's the one lesson I'd steal. A second one: I let the product say a student's grades "sit in range" when for most schools I only had how selective the school is. It now says what each result is based on. Where it is: live, free, and I'm trying to get the first 50 families all the way through a full session. Question for you: if you've built on an LLM, how do you stop it stating numbers it shouldn't? And where did your first 50 users come from? If you’ve got a high school kid, esp juniors, I’d love your feedback… it’s free: https://helloaiko.co 🙏🏼   submitted by   /u/-DangerMouse- [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/12 12:49:00