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How a Retry-Repair Pipeline Solves LLM Structured Output Failures Reliably

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A developer at Shpper encountered recurring JSON parsing failures after deploying an LLM-powered product feature, with the model producing malformed output often enough to cause real production incidents. Common failure modes include markdown fences, conversational preamble, trailing commas, type drift, and missing or hallucinated fields. The author outlines a three-tier approach to enforcing structured output: constrained decoding with JSON Schema, tool or function calling, and prompt-based validation as a last resort. Even the strongest provider-level constraints only guarantee syntactic validity, not semantic correctness, making downstream validation still necessary. The recommended solution is a retry-repair loop that handles malformed responses architecturally rather than patching individual failure cases as they arise.

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How a Retry-Repair Pipeline Solves LLM Structured Output Failures Reliably · ShortSingh