Why LLM Decisions Should Be Deterministic

Short summary

The author argues that deterministic boundaries around LLMs provide auditability, not just consistency. By enforcing decisions in deterministic code (e.g., validating model output against a fixed enum) rather than letting the model decide freely, every decision becomes reproducible from source code alone. This matters for AI governance and regulatory compliance, especially under frameworks like the EU AI Act, where third parties must be able to inspect and reconstruct how decisions were reached.

• •Deterministic validation of LLM output enables reproducible, auditable decisions
• •Decisions should live in code, not in the model — LLM narrates but doesn't decide
• •This pattern aligns with emerging AI governance and EU AI Act audit requirements

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