You will build a working eval system for YOUR product. By the end of this course, you ship a calibrated eval suite — built on your own product's data — that you can run on Monday morning. Not a toy. Not a demo. A working pipeline with documented precision, integrated into your existing workflow, with a stakeholder memo explaining what it found. **The arc**: broken AI output → root cause → evaluator → pipeline → stakeholder action. Each module's output becomes the next module's input. Nothing is discarded; everything compounds. **By the end of this course you will be able to:** 1. Analyze AI product failures by systematically categorizing error types using open/axial coding on production outputs. 2. Create binary pass/fail evaluators (LLM-as-judge and code-based) calibrated to ≥80% precision against human labels. 3. Evaluate your AI product's quality by composing evaluators into a test suite, interpreting results, and recommending actions to stakeholders. Every deliverable uses YOUR product data. Every artifact goes to work with you. The final package — error taxonomy, calibrated evaluators, pipeline script, and stakeholder memo — is production-ready the day you finish.
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