I watched a resume tailoring agent run 50 perfect demos. Every test resume produced clean, accurate output. On the first real user submission, the model fabricated a degree, invented a job title, and added a company that didn't exist. The demo never caught it because the demo data was curated. Production data is never curated.
The problem wasn't the model. It was the contract.
Most teams treat the LLM as a black box. Feed it text, get text back, move on. When it fails in production, they blame the model. But the model is doing exactly what you asked. The fault is in the system prompt you never read, the schema you never validated, and the fallback you never built.
Here's what I've learned building production AI pipelines that don't fall apart when real users show up.
The demo trap
