Developing and Evaluating LLM Features for Product Teams

Integrating Large Language Models (LLMs) into your product requires more than just writing a prompt; it demands systematic development and rigorous evaluation. This text-based course guides you through the entire lifecycle of LLM feature creation, from initial concept to quality assurance. You will transition from guessing if your AI features work to confidently measuring their performance, accuracy, and cost-efficiency. Through detailed written explanations and structured examples, you will master the methodologies needed to ship reliable LLM-powered capabilities. What you'll learn: Understand foundational LLM concepts, terminology, and integration patterns; Design effective prompts and implement retrieval-augmented generation (RAG) workflows; Establish systematic evaluation frameworks to measure output quality and accuracy; Apply modern validation techniques, including using LLMs as judges for automated testing; Optimize LLM features for latency, cost, and rate-limiting constraints; Collaborate across product, engineering, and QA roles to maintain high-quality AI features. The course begins with core definitions and terminology before moving into hands-on prompt design patterns and systematic evaluation strategies. You will read through practical scenarios and analyze structured code snippets to build a robust testing workflow. This course is designed for product managers, QA specialists, and developers who are new to building with LLMs. No prior machine learning experience is required. Start reading today to build and scale reliable LLM features with confidence.