Course · Intermediate
Evaluating LLM Outputs
Once a RAG system works, we need to ask whether its answers are any good, and reading every answer by hand doesn't scale or stay consistent. This course turns output quality into numbers you can track, compare, and act on. You'll separate retrieval failures from generation failures, score answers with both deterministic metrics and an LLM judge, calibrate that judge so you know how far to trust it, and monitor a live pipeline for drift, cost, and latency so you catch regressions before your users do.
- Intermediate friendly
- 6 hrs
- 3 lessons
- Premium
Course overview
Learn to evaluate LLM and RAG outputs in Python, covering retrieval and generation metrics, LLM-as-judge evaluation, calibration, and production monitoring for drift, cost, and latency.
What's inside
3 lessons
- 01 120 minFoundation Metrics and Evaluation Frameworks
Learn to evaluate LLM outputs systematically using retrieval metrics (recall@k, MRR), citation accuracy, lexical scores (BLEU, ROUGE), and answerability checks. Build evaluation datasets, use argparse for CLI scripts, and understand the RAGAS framework for RAG system assessment.
- 02 120 minLLM-as-Judge and Automated Evaluation
Learn to build, test, and calibrate an LLM judge that scores RAG answers by reading for meaning. Build both heuristic and LLM versions, understand their failure modes, and see how RAGAS and DeepEval package the same idea.
- 03 120 minProduction Evaluation and Observability
From single answer evaluation to monitoring populations of runs over time. Build instrumented pipelines that track latency, tokens, and cost. Learn why green dashboards can hide failures and how to build trustworthy monitoring with baselines, versioning, and drift detection.
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