Building AI Applications with Semantic Kernel and RAG

Modern AI applications require reliable ways to integrate large language models with external data and existing business logic. This text-based course guides you through orchestrating AI workflows using Semantic Kernel, enabling you to build responsive systems with dynamic prompt templates and context-aware retrieval. You will begin with essential terminology and core architectural concepts before moving into practical code implementations. By reading clear technical explanations and analyzing functional code samples, you will gain confidence in structuring prompts, connecting native functions, and querying vector embeddings. What you will learn: - Understand the core concepts of Semantic Kernel and AI orchestration - Create and manage dynamic prompt templates for tailored model interactions - Implement retrieval-augmented generation (RAG) to ground responses in custom knowledge - Configure plugins and native functions to extend language model capabilities - Connect vector stores and manage embeddings for semantic search - Apply modern best practices for token management, error handling, and prompt design The course begins with foundational theory and syntax, moving step-by-step into advanced orchestration patterns and modular system design. Each concept is accompanied by written code examples and practical reading exercises. This course is designed for programmers and technical learners who want to build sophisticated AI workflows without requiring previous machine learning experience. Start reading today to expand your software development toolkit with modern AI orchestration skills.