How enforced JSON schemas and validator feedback loops cut LLM output errors

A software team building LLM-powered pipelines adopted a strict policy of validating all model outputs against JSON schemas rather than relying on prompt instructions alone. When a model's output fails validation, the exact error message is fed back to the model for a retry, a technique that proved far more effective than generic retry prompts. Replacing open-ended string fields with closed enumerations reduced one pipeline's rejection rate from 4.1% to 0.2%. Outputs that fail after three attempts are routed to a human review queue, and those labeled cases are added to a regression test suite. The team concludes that schema design is itself a form of prompt engineering, since removing output degrees of freedom directly reduces the scope for model hallucinations.