How do you design semantic correctness validation in LLM pipelines?

Semantic validation ensures LLM outputs are meaningfully correct using embedding similarity, rule checks, and verifier models.

Unlike syntactic validation, semantic correctness ensures the meaning of output aligns with expected intent. This is done using embedding similarity checks against reference answers, entailment models, and LLM-based evaluators. It is critical in knowledge-heavy systems like enterprise search and assistants.