A 5-Layer Architecture for Production AI Quality Assurance: Input Validation, Output Checking, and Behavioral Monitoring

Short summary

The author describes a 5-layer AI quality architecture built across five production AI systems, arguing traditional binary QA fails for probabilistic AI outputs. Layer 1 enforces input validation via Pydantic schemas, RAG quality thresholds with circuit breakers, and prompt version gates evaluated against adversarial golden datasets. Layer 2 runs deterministic pattern checks, with subsequent layers addressing behavioral evaluation, drift detection, and cost monitoring. The article includes code examples for each gate and notes a real incident where automated prompt rollback prevented degraded teaching quality.

• •Layer 1 gates validate inputs before LLM calls: Pydantic schemas, RAG similarity thresholds with circuit breakers, and prompt version control with adversarial golden datasets
• •Layer 2 runs deterministic pattern checks on LLM outputs to catch forbidden or missing patterns without additional LLM calls
• •Author reports a real production incident where automated prompt rollback (v2 score 0.60 blocked, v1 score 0.93 restored) prevented user-facing quality degradation

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