Developing LLM Applications with Prompt Flow

Building reliable applications with large language models requires more than just writing prompts; it demands structured orchestration, systematic evaluation, and robust workflows. This text-based course guides you through the fundamentals of Prompt Flow, enabling you to prototype, test, and deploy sophisticated AI-driven solutions. You will transition from writing simple prompts to managing complex LLM workflows, integrating external data sources, and measuring application performance using modern evaluation metrics. What you'll learn: - Understand the core concepts of Prompt Flow, including LLM tools, python tools, and prompt nodes. - Build executable workflows that link language models, custom code, and database queries. - Implement Retrieval-Augmented Generation (RAG) patterns to ground model responses in your own data. - Evaluate application performance using structured metrics like accuracy, relevance, and safety. - Configure connections to cloud-based language models and vector databases securely. - Deploy and monitor your LLM workflows as web APIs for real-world integration. The course begins with foundational AI terminology and Prompt Flow basics before guiding you step-by-step through building, testing, and optimizing your first multi-node LLM application. Designed for software developers, data enthusiasts, and AI beginners, this course requires no prior experience with machine learning or prompt orchestration tools. Start reading today to master the workflows powering modern language model applications.