Testing LLM applications requires different approaches due to probabilistic outputs. Testing pyramid: unit tests for pure functions and prompt templates (no LLM calls needed), component tests for prompt generation and output parsers, integration tests for LLM + tools + database, and E2E tests for user flows. Use DeepEval for LLM evaluation with AnswerRelevancyMetric, FaithfulnessMetric, and HallucinationMetric. Build regression suites with golden datasets - curated test cases with expected outputs, evaluate using LLM-as-judge for semantic similarity. Key insight: never compare LLM outputs with exact string matching - always use semantic similarity or LLM evaluation. Set acceptable thresholds and alert when degraded.