LLM output validation ensures responses are structurally correct (valid JSON, expected fields), factually grounded (claims supported by retrieved context), and appropriate for users. Without validation, malformed JSON crashes downstream systems, hallucinated facts mislead users, and inappropriate content violates policies. Structured output mode (OpenAI) + Zod schema validation is the first line of defence. Hallucination detection uses reference-checking against the source context.
Automatically repairing malformed LLM output.
Regular JSON mode asks the model to output JSON — but it can still output malformed JSON or wrong schema. Structured Output mode with a JSON Schema definition uses constrained decoding to guarantee valid JSON and correct field structure. Use it for any production structured output.
When Zod validation fails, send the error back to the LLM: 'Your response had this error: [error]. Please fix and respond with valid JSON only.' This catches 80% of schema failures without complex parsing heuristics.
LLMs confidently state false information. For RAG applications, every key claim should be verifiable against the retrieved context. Serve responses with a confidence/grounded flag to the UI — show 'low confidence' warnings to users.
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