Deterministic Code, Not LLMs, Should Own System Decisions for True Auditability

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A software developer argues that the deeper value of wrapping LLM outputs in deterministic code is auditability, not just consistency. When decisions are made by reproducible Python functions rather than a language model, any third party can inspect and re-run the exact logic months later. The author illustrates this with an alert classification system that validates model output against a fixed eight-value set, falling back to 'unknown' if the output does not match. This approach mirrors standard data-validation discipline applied to any external system, such as third-party webhooks or API responses. The author notes that much of the emerging AI governance tooling market is converging on this same property from a regulatory direction.

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ProgrammingDEV Community ·

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