Deterministic Verification: Why Prompts Fail in Production Systems
A prompt is not a product. How Axiom & Alloy builds deterministic test harnesses and schema validation layers around probabilistic LLMs before output touches customers.
Large language models are probabilistic token predictors. They do not know whether a mathematical calculation is exact, whether a JSON payload adheres to a strict schema, or whether an extracted legal fact is hallucinated.
Treating an unconstrained prompt as a production software backend is a critical engineering error. In our lab, every generative component is enveloped by a deterministic verification harness.
We apply three strict engineering rules: First, schema enforcement using Zod and type guards on all model outputs. If a model fails schema validation, the output is rejected or automatically repaired before touching downstream databases. Second, isolated unit checks that test deterministic boundaries. Our tool 'n8n-lint' (under She Runs Code) verifies automation workflows before deployment so failures do not propagate silently.
Third, subagent ranking. Under our Agent Rerank pipeline, tasks are evaluated against historical benchmark performance. The wrong agent is demoted; the system never relies on a single model's unvalidated guess.