Deterministic-LLM Sandwich

Problem

Trusting the model's output unconditionally accepts hallucination at exactly the moment where mistakes are most expensive, and there is no signal at the boundary distinguishing a correct generation from a confidently wrong one. Banning the model entirely loses everything it was good at and forces the team back to brittle templated text. Simple downstream validation (a try/catch on the database call, for example) catches some failures but only after side effects have begun or only by failing loudly to the user. The team needs a way to keep the model in the loop while bounding what kinds of output it can land.

Solution

Three layers. Pre: deterministic check decides whether the LLM should run at all (e.g. AST parse must succeed). LLM: produces a candidate output with structured-output schema and frozen rubric. Post: deterministic re-validation (parse, type-check, run tests). If post fails, the original is returned unchanged.

When to use

• LLM output must be checked deterministically before being trusted (e.g. AST parse, type-check, test run).
• A pre-check can decide whether the LLM should run at all.
• Returning the original input on post-check failure is acceptable behaviour.