Original Summary

Anyone can build an AI prototype in an afternoon now. You connect an API, prompt Cursor or ChatGPT, and it works on your machine. You honestly think you're 80% done. Then you let actual users touch it, and everything falls apart. Over the past year of building and debugging AI apps, these are the 3 exact failure points that break prototypes every single time: Relying on prompts for JSON formatting Telling an LLM "output valid JSON" works 95% of the time. The other 5% of the time, it wraps it in markdown backticks ( `json ), misses a closing bracket, or hallucinates an extra key. If your backend uses standard JSON.parse(), your server throws an unhandled crash. The fix: Never trust raw model outputs. Always run them through defensive runtime schema validation (like Zod safeParse in TypeScript or Pydantic in Python) with an automated fallback. Unbounded retry loops If an agent fails a task, the intuitive reaction is: while (!done) { re-prompt with error }. In production, this is how you burn $50 in 10 minutes. The model hallucinates the exact same syntax error 4 times in a row, burns tokens, and crashes. The fix: Hard iteration ceilings (max 3 turns). If it doesn't converge, roll back state to the last clean checkpoint and fail gracefully. Context dumping (stuffing raw files into prompts) Feeding 500 lines of code or raw PDF chunks into the prompt degrades attention. The model misses subtle constraints in the middle of the prompt. The fix: Index exact relationships and symbols. Feed the model the exact 10 lines governing the decision, not the entire file. Prototypes run on optimism. Production software survives on defensive engineering. What’s the #1 bug or headache you’ve run into when trying to take an AI project past the demo stage?   submitted by   /u/akshat_code007 [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 · SaaS
  • 发布时间：2026/9/27 18:19:09