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AI Syllabus Module

Module 1.11: LLM Evaluation

Design diagnostic evaluation metrics checking hallucination counts, faithfulness, and CI/CD validation checks.

Lessons & Submodules

Why LLM Evaluation Matters

Understand the risks of manual verification and prompt updates.

Golden Datasets

Curate representative datasets of user prompts and ideal answers.

Accuracy, Precision and Recall

Measure accuracy on classifications and structured completions.

Faithfulness and Hallucination Checks

Detect hallucinated statements in RAG outputs using source references.

LLM-as-a-Judge

Design LLM evaluation templates with robust grading rubrics.

Regression Testing in CI/CD

Integrate evaluation checks into automated deployment pipelines.

Evaluation Interview Guide

Prepare for engineering interviews focused on evaluation systems.

Key Skills

  • •Construct a gold-standard dataset for prompt regression tests
  • •Set up automated eval actions in CI/CD build scripts

Interview Value

  • How do you evaluate semantic faithfulness on dynamic, open-ended LLM outputs at scale?