Evaluating Generative AI: Essential Metrics for LLMs and Image Models

As generative AI moves from experimental prototypes to production systems, knowing how to measure model performance accurately is critical. Without robust evaluation, it is impossible to know if your AI application is improving, hallucinating, or delivering safe, high-quality results. This course guides you through the foundational concepts and modern methodologies used to assess generative AI outputs. You will transition from understanding basic statistical scores to implementing advanced evaluation frameworks for text, images, and retrieval-augmented generation (RAG) systems. What you'll learn: - Understand foundational evaluation terminology, including precision, recall, and the unique challenges of assessing generative outputs. - Measure text generation quality using classic automatic metrics like BLEU, ROUGE, and semantic similarity scores. - Evaluate image synthesis models using standard benchmarks such as Frechet Inception Distance (FID). - Implement modern LLM-as-a-judge workflows to automate qualitative feedback safely and efficiently. - Assess Retrieval-Augmented Generation (RAG) pipelines for faithfulness, answer relevance, and context recall. - Design structured human evaluation frameworks and qualitative feedback loops to validate automated metrics. You will start with core definitions and the theory of evaluation, establishing a solid conceptual foundation. From there, you will explore text and image metrics step-by-step, concluding with contemporary techniques for evaluating modern large language model applications. This course is designed for beginners, developers, and product managers who want to understand how to measure generative AI quality. No advanced programming or machine learning background is required. Read through our structured guides and start building reliable, measurable generative AI systems today.