Fixing Silent LLM Data Corruption Through Corrective Prompting

LLM-powered APIs appear to produce valid JSON until deployed at scale, where they return deceptively malformed responses that silently bypass error handling. The author demonstrates corrective prompting—automatically feeding invalid responses back to the model with explicit error feedback—which recovers 90% of failures without additional API calls, making this essential knowledge for any production LLM system extracting structured data.