The Schema Problem: Taming LLM Output in Production
· 9 min read
You ship a feature that extracts structured data from user text using an LLM. You test it thoroughly. It works. Three months later, a model provider quietly updates their weights, and without changing a single line of your code, your downstream pipeline starts silently dropping records. No exceptions thrown. No alerts fired. Just wrong data flowing through your system.
This is the schema problem. And despite years of improvements to structured output APIs, it remains one of the least-discussed failure modes in LLM-powered systems.
