ai-observability-promptfoo

# Promptfoo Patterns > **Quick Guide:** Use promptfoo for systematic LLM evaluation. Define prompts, providers, and test cases in `promptfooconfig.yaml`. Use assertion types (`contains`, `is-json`, `llm-rubric`, `similar`, `cost`, `latency`) to validate outputs. Use `promptfoo eval` to run (exits with code 100 on test failures), `promptfoo view` for results UI. Use model-graded assertions (`llm-rubric`, `factuality`) for subjective quality. Use `promptfoo redteam run` for security scanning. Use `--share` flag or `promptfoo share` to share results. All provider API keys come from environment variables -- never hardcode them. --- <critical_requirements> ## CRITICAL: Before Using This Skill > **All code must follow project conventions in CLAUDE.md** (kebab-case, named exports, import ordering, `import type`, named constants) **(You MUST define test cases with explicit `assert` arrays -- tests without assertions only capture output without validating it)** **(You MUST use `llm-rubric` for subjective quality evaluation -- do NOT rely solely on deterministic assertions for natural language output)** **(You MUST set `threshold` on similarity and model-graded assertions -- omitting thresholds uses defaults that may not match your quality bar)** **(You MUST use environment variables for all API keys -- never hardcode keys in promptfooconfig.yaml or provider configs)** **(You MUST verify `promptfoo eval` exit code in CI pipelines -- it returns exit code 100 on test failures, ...