How do you implement AI integration testing and validation pipelines?

AI integration testing validates the full pipeline from data input through AI processing to output delivery. Testing layers: unit tests (individual functions with mocked AI responses), integration tests (end-to-end pipeline with recorded AI outputs), contract tests (verify API schemas between services), and validation tests (check AI output quality against golden datasets). Implementation: create a test fixture library of representative inputs spanning edge cases, record AI provider responses for deterministic replay (avoid flaky tests from non-deterministic AI), implement golden dataset comparison for regression detection, and test error handling for AI provider timeouts, rate limits, and errors. Validation pipeline: after each model or prompt change, run the full test suite against a golden dataset of 200+ examples, compute quality metrics (accuracy, F1, BLEU, human evaluation scores), compare against baseline, and gate deployment on metric thresholds. CI/CD integration: run quick smoke tests on every commit (subset of golden dataset), full validation suite on merge to main, canary deployment with production traffic monitoring, and automated rollback if quality metrics degrade. Specific test scenarios: test prompt injection resistance, verify PII handling (detection and masking), test rate limiting and backpressure behavior, validate response format consistency, and test graceful degradation when AI services are unavailable. Mock strategies: use deterministic seed values where supported, record and replay responses using VCR-like libraries, and implement an AI testing interface that returns predictable outputs for known inputs.