Original Summary

I built FaultGym, a browser-based practice lab for investigating failures in AI systems. The first incident starts with an assistant quoting a policy accurately, but reaching the wrong decision because it retrieved a version that doesn't apply to that contract. You get the incident, documents, and execution trace, then change the pipeline configuration and test your repair. There are three labs in the public beta: - Wrong policy-version selection in RAG - Customer isolation across retrieval and caching - Prompt injection through documents and unsafe tool authorization These are fictional training scenarios with synthetic data and simulated LLM behaviour. The configuration changes run against the lab engine, with visible practice cases and held-out grading cases. You don't need an API key or signup for simulated practice, and it's free to try. https://faultgym.com/ I'm trying to work out who this helps most: engineers who have already built a basic RAG app, or college students learning AI engineering. Would you use incident-repair exercises like this, and what would make the learning useful enough to come back for another lab? The video is a 30-second illustrated overview of the workflow.   submitted by   /u/SpringThese9004 [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：2026/9/23 22:39:42