How one platform tamed an unpredictable AI model inside a deterministic pipeline

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A software platform integrated a large language model into its production chain to generate code for customer apps, despite LLMs being inherently non-deterministic. Rather than trusting the model to follow instructions, engineers built a strict validation layer that treats every model output as a proposal subject to mechanical inspection. When validation fails, the system attempts cheap, deterministic repairs first before making targeted model correction calls, reserving full regeneration as a last resort. The model is also constrained in correction mode to prevent it from reframing failures as conversational responses, which would otherwise mask real regressions in telemetry. The approach draws a clear line between real-time generation, where speed limits testing options, and offline pre-generation, where multiple candidates can be scored by a judge model to select the best result.

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