How do you design correctness guarantees in probabilistic LLM systems?

Correctness in LLM systems is achieved through layered validation, constraints, verification models, and deterministic fallback paths.

Since LLMs are probabilistic, correctness cannot be guaranteed at generation time. Instead, systems enforce correctness through multiple layers: constrained decoding, schema validation, retrieval grounding, verifier models, and rule-based post-processing. For high-stakes domains, outputs are only accepted if they pass all validation gates, otherwise they are regenerated or downgraded to deterministic systems.