Evaluating Multimodal AI: Text-to-Image Benchmarking with MuLan

Generating high-quality images from text is only half the battle; understanding how well an AI model actually follows complex prompts is the real challenge. This text-only course guides you through the foundational concepts of evaluating multimodal AI models, focusing on the MuLan framework, compositional benchmarking, and modern agentic evaluation workflows. You will learn to systematically analyze how AI interprets detailed prompts and where it falls short. What you'll learn: - Understand foundational concepts of multimodal AI evaluation and why traditional metrics fall short. - Learn how the MuLan framework assesses complex, multi-subject text-to-image tasks. - Apply compositional benchmarks to measure spatial relationships, attributes, and actions in generated images. - Explore agentic design patterns that use multi-step reasoning to evaluate and refine AI outputs. - Practice designing custom evaluation rubrics using modern LLM-as-a-judge methodologies. You will start with core terminology and benchmarking fundamentals before walking through step-by-step evaluation methodologies and agentic design insights. This course is designed for beginners in AI engineering, product management, or data science, with no prior experience in multimodal evaluation required. Start reading today to master the art of measuring and improving multimodal AI performance.