LLM Evaluation Metrics: Practice Tests

About this course

This course, LLM Evaluation Metrics: Practice Tests, is designed to help learners understand how Large Language Models (LLMs) are evaluated in real-world applications. As LLMs become central to AI systems, evaluating their performance, reliability, safety, and usefulness is essential. This course provides a structured and practical approach to mastering LLM evaluation from basic to advanced concepts.

You will learn key evaluation metrics used in NLP and LLM systems, including accuracy, precision, recall, BLEU, ROUGE, perplexity, and semantic similarity metrics. The course also covers advanced evaluation techniques such as human evaluation, RAG (Retrieval-Augmented Generation) evaluation, hallucination detection, bias measurement, and safety assessment.

In addition, you will gain knowledge of production-level evaluation concepts such as latency, throughput, cost efficiency, monitoring, and drift detection. The course also introduces enterprise-focused evaluation areas like governance, compliance, business impact, and ROI analysis.

By the end of this course, you will be able to confidently evaluate LLM outputs, compare different models, and understand how to improve AI system performance in real-world environments.

Key Learning AreasFundamentals of LLM evaluation and performance measurement

Core NLP metrics such as BLEU, ROUGE, accuracy, and perplexity

Human evaluation techniques for quality assessmentRAG evaluation and retrieval performance analysis

Safety, bias, and hallucination detection methods

Production monitoring including latency, throughput, and drift

Enterprise evaluation including ROI, governance, and compliance

This course is ideal for students, developers, data scientists, and AI professionals who want to build strong practical skills in evaluating and improving LLM-based systems.

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