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Building LLM Evaluation Systems for Production AI

7 chapters · 32 lessons

1. 1. Defining Evaluation Scope and Use Cases

   4 lessons

   Map product requirements to testable behaviors and identify what good looks like for your specific LLM application.

2. 2. Building Representative Test Sets

   4 lessons

   Create test datasets that capture real-world diversity, edge cases, and adversarial inputs for your domain.

3. 3. Writing Scoring Rubrics and Metrics

   5 lessons

   Define explicit, measurable criteria for correctness, safety, faithfulness, and task-specific quality.

4. 4. Implementing LLM-as-Judge Pipelines

   5 lessons

   Build scalable automated evaluation systems using LLMs as judges, with calibration and quality controls.

5. 5. Evaluating RAG Systems End to End

   5 lessons

   Separate retrieval quality from generation quality and measure faithfulness, citation, and refusal behavior.

6. 6. Running Error Analysis and Regression Testing

   4 lessons

   Turn evaluation results into insights and build safety nets that catch regressions when systems change.

7. 7. Evaluation in Deployment and Production

   5 lessons

   Extend evaluation to live environments, monitor drift, and adapt to device, latency, and user context constraints.