Build practical skills for evaluating, optimizing, securing, and deploying production-ready Large Language Model (LLM) applications. Designed for learners who want to move beyond basic LLM development, this course focuses on the methods needed to improve AI quality, efficiency, security, and production performance.

You’ll learn to evaluate LLM outputs using human and automated techniques to measure response quality and reliability. You’ll apply token optimization and semantic caching to improve efficiency and reduce costs, while using prompt injection prevention techniques to strengthen LLM application security. You’ll then bring these skills together in an end-to-end capstone project. You’ll select a practical use case, build an LLM application, deploy it to the cloud using Render, and optimize the code for scalable, maintainable production environments. By the end of the course, you’ll be able to evaluate LLM quality, optimize application performance, strengthen security, and deploy production-ready LLM applications. Its combination of evaluation, optimization, security, deployment, and hands-on application development provides a practical path from LLM development to production.