Production LLM applications require comprehensive output validation. Core techniques: 1) Pydantic output parsers with validators for structure enforcement. 2) Guardrails AI for content safety checks including PII detection, toxicity filtering with configurable on_fail actions. 3) Retry logic with tenacity library using exponential backoff - include error context in retry prompts. 4) Content safety filters using regex patterns to redact sensitive information. 5) Hallucination detection via LLM-as-judge: have a second call verify generated claims are supported by provided context. 6) Production monitoring with Langfuse - track validation pass rates, violation types, and retry rates.