Back to Module 1.11: LLM EvaluationComing Soon
AI Lesson & Submodule
Accuracy, Precision and Recall
Measure accuracy on classifications and structured completions.
Why This Matters
Traditional string overlap metrics fail to measure conceptual accuracy in open-ended generations.
What You Will Learn
- •Calculate precision scores
- •Evaluate recall performance
- •Classify confusion matrices
Concepts Covered
Classification accuracy metricsPrecision-Recall balancesConfusion matrix logs
Mapped Foundation Project: LLM Evaluation Lab
Regression evaluation dashboard testing prompt templates against golden datasets, checking faithfulness and schema compliance.
Architecture Preview
CI/CD testing pipeline running prompt variants against tests, compiling scores, and flagging failures.
Prompt Template CommitGolden Dataset CollectionModel Completion Loop
Tech Stack Planned
TypeScriptVitestReact
GitHub: Coming SoonLive Demo: Coming Soon
Coming SoonTechnical Interview Value
- ?Why do traditional NLP metrics (like BLEU or ROUGE) fail when evaluating LLM summaries?