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Learn to evaluate RAG pipelines with Recall@k, MRR, NDCG for retrieval and faithfulness, relevance, hallucination rate for generation. Includes RAGAS setup.
Master every classification and regression metric from accuracy and F1 to AUC-ROC and confusion matrices, with Python code and guidance on when to use each.
Learn how to measure prompt quality with evaluation datasets, scoring rubrics, A/B testing, and automated grading to iterate on prompts with evidence.
Master LLM evaluation with automated metrics like BLEU and ROUGE, plus human evaluation frameworks for measuring quality, safety, and reliability.
A practical overview of evaluation frameworks for AI applications: what they measure, how they differ, and how to pick one that matches your workflow.
How LLMOps differs from classical MLOps: evaluation, prompts as code, drift, cost, and the workflows that actually work in production.
A thorough look at the confusion matrix: how to read it, the metrics it produces, and how to use it to diagnose classifier behavior beyond a single accuracy number that often hides what is going wrong.
Measure RAG quality with recall@k, MRR, context precision, faithfulness, and answer relevancy so you can iterate on data, not vibes.
Use LangSmith to trace, debug, and evaluate RAG pipelines step by step, from instrumentation to dataset replay and regression detection.