Originally published on AI Tech Connect.
The failure-rate problem Every team that has put an LLM into production for data extraction, classification, or structured reporting eventually hits the same wall: the model does not return valid JSON. Sometimes it prepends an explanation ("Here is the JSON you requested:"). Sometimes it wraps the object in a Markdown code fence. Sometimes it uses single quotes. Sometimes it simply truncates the response at the token limit, leaving you with malformed JSON that crashes json.loads(). Production deployments commonly report parse failure rates in the low-to-mid double digits on prompt-only JSON extraction — rates that rise further for deeply nested schemas with four or more levels of nesting, and higher still for schemas that include polymorphic union types. Even a modest failure rate means a…
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