Your LLM Passes Type Checks but Fails the "Vibe Check": How I Fixed AI Reliability

Your LLM Passes Type Checks but Fails the "Vibe Check": How I Fixed AI Reliability You validate your LLM outputs with Pydantic. The JSON is well-formed. The fields are correct. Life is good. Then your model returns a "polite decline" that says "I'd rather gouge my eyes out." It passes your type checks. It fails the vibe check. This is the Semantic Gap — the space between structural correctness and actual meaning. Every team shipping LLM-powered features hits it eventually. I got tired of hitting it, so I built Semantix. The Semantic Gap: Shape vs. Meaning Here's what most validation looks like today: class Response(BaseModel): message: str tone: Literal["polite", "neutral", "firm"] This tells you the shape is right. It tells you nothing about whether the meaning is right. Your model can return {"message": "Go away.", "tone": "polite"} and Pydantic will happily accept it. Semantix flips the script. Instead of validating structure, you validate intent: from semantix import Intent, valida