Eval Harness

Build a reproducible evaluation harness for LLM pipelines, AI features, or agent workflows. The harness consists of:

• Eval definitions — test cases with inputs, expected outputs, and scoring rubrics
• Runner — executes the pipeline against all test cases
• Scorer — applies rubrics and records results
• Tracker — maintains pass/fail history across runs (via SQL session DB)

When to Use

• Building a new LLM-powered feature and want regressions caught automatically
• Changing prompts and want to confirm no quality degradation
• Demonstrating quality evidence for a shipped AI pipeline
• Setting a quality gate for a CI/CD pipeline

When NOT to Use