This module explores the essential strategies and tools for evaluating and testing large language model (LLM) agents in real-world applications. Learners will discover how to assess agent capabilities, system integration, and output quality using both offline and benchmark-based methods. Practical examples illustrate how to measure correctness, tone, and the overall value delivered to users and stakeholders.

This module guides learners through the essential steps for deploying large language model (LLM) applications into production, emphasizing best practices for scalability, reliability, and observability. Learners will explore deployment frameworks such as Ray Serve, Docker, and Kubernetes, as well as strategies for monitoring, bias detection, and cost management. The module also introduces emerging standards like the Model Context Protocol and specialized platforms for managing LLM workflows.

This module explores the evolving landscape of generative AI, examining the limitations of scaling models and the emergence of alternative approaches. Learners will investigate the impact of generative AI on industry, workforce dynamics, and broader societal implications. By the end, participants will gain insights into the future directions and challenges of AI development.