Designing constraint-first generation with LLMs — how to prevent invalid output by design?

I’m working on a system that uses LLMs for generation, but the goal is explicitly not creativity.

The goal is: deterministic, error-resistant output where invalid results should be impossible, not corrected afterwards.

What I’m trying to avoid: •generate → lint → fix loops •post-hoc validation •probabilistic “good enough” outputs

What I’m aiming for instead: •constraint-first generation •explicit decision trees / rule systems •abort-on-violation logic •single-pass generation only if all constraints are satisfied

Think closer to: compilers, planners, constrained generators — not prompt engineering.

Questions I’m stuck on:

-Architectural patterns to enforce hard constraints during generation (not after)

-Whether LLMs can realistically be used this way, or if they should only fill predefined slots

-How you would define and measure “success” in such systems beyond internal consistency

-Where you personally draw the line between engineering guarantees vs accepting probabilistic failure

Not looking for tools or prompt tricks. Interested in system-level thinking and failure modes.

If you’ve worked on compilers, infra, ML systems, or constrained generation, I’d value your take.