Generative AI: Effective Evaluation and Debugging

Generative AI offers incredible potential, but building reliable and effective applications requires more than just model training. Ensuring your models perform as expected and identifying issues quickly is paramount for successful deployment. This course will equip you with the essential methods and tools to systematically evaluate, debug, and improve your Generative AI models, transforming your ability to deliver high-quality AI solutions. What you'll learn: * Understand core metrics and human-in-the-loop strategies for evaluating Generative AI outputs. * Debug common Generative AI issues like hallucinations, bias, and lack of coherence. * Apply fundamental MLOps principles for experiment tracking and versioning Generative AI models and prompts. * Implement basic guardrails and safety mechanisms to ensure responsible AI application behavior. * Learn how prompt engineering techniques impact model performance and evaluation results. * Practice interpreting model outputs and systematically identifying areas for improvement. This course begins with foundational concepts of Generative AI evaluation, moving through practical debugging strategies, and concluding with an introduction to MLOps techniques for managing and improving your AI projects. This course is designed for beginners with a basic understanding of machine learning concepts who want to build and deploy reliable Generative AI applications. No prior experience with Generative AI evaluation or debugging is required. Start building more reliable and trustworthy Generative AI solutions today.