LLM Evaluation: Assessing Performance, Cost, and Efficiency

Deploying large language models is only the first step; the real challenge lies in ensuring they perform accurately without draining your budget. This text-based course teaches you how to systematically evaluate LLM outputs and optimize their efficiency for production. You will transition from guessing how well your model works to confidently measuring its success using industry-standard metrics. By understanding the critical trade-offs between model size, latency, throughput, and cost, you will learn to make data-driven decisions that align with both technical and business goals. In this course, you will: Learn foundational LLM evaluation metrics for accuracy, relevance, and safety; Measure latency, throughput, and operational costs to optimize system efficiency; Apply modern evaluation techniques, including the LLM-as-a-judge pattern and automated benchmarks; Evaluate Retrieval-Augmented Generation (RAG) systems for hallucination and retrieval quality; Analyze the trade-offs between proprietary APIs and hosting open-source models; Design a structured evaluation pipeline to continuously monitor model performance. The course starts with core definitions of LLM metrics before guiding you through practical frameworks for measuring latency, cost, and output quality. You will read through clear explanations, conceptual breakdowns, and written exercises designed to build real-world decision-making skills. This program is built for product managers, aspiring ML engineers, and technical leads looking for a clear, beginner-friendly introduction to LLM operations with no advanced programming prerequisites. Start reading today to master the art of building reliable, cost-effective LLM applications.