Evaluating Foundation Model Performance with Bedrock

Selecting the right foundation model for your application requires more than just guesswork; it demands rigorous, data-driven evaluation. This course provides a clear, structured path to understanding how to assess and compare large language model performance using Bedrock tools and industry-standard methodologies. You will learn how to align model capabilities with your specific business goals, ensuring optimal performance and cost-efficiency. By completing this course, you will transition from basic experimentation to implementing structured evaluation workflows. You will gain the skills to set up automated benchmarks, design human-in-the-loop evaluation pipelines, and interpret key performance metrics to make informed deployment decisions. What you'll learn: - Understand the core principles of foundation model evaluation and key performance metrics - Configure automated evaluations using standard benchmark datasets within Bedrock - Design and manage human evaluation workflows to assess qualitative aspects like tone and alignment - Set up systematic comparison runs to evaluate different models side-by-side - Apply modern retrieval-augmented generation (RAG) evaluation techniques to measure groundedness and relevance - Interpret evaluation reports to optimize model selection for production readiness This course begins with foundational concepts, defining key terminology and evaluation metrics before moving into practical configuration steps. You will explore a logical progression from automated metric-based testing to human-led qualitative reviews, culminating in real-world selection strategies. This course is designed for software developers, data specialists, and technology decision-makers who are new to model evaluation. No prior experience with machine learning pipelines or advanced statistics is required. Start reading today to build a reliable framework for measuring and optimizing your generative AI applications.