Building RAG Applications with Semantic Kernel and MongoDB

Modern AI applications require grounding large language models with your own proprietary data to prevent hallucinations and provide highly relevant answers. Harnessing the power of Retrieval-Augmented Generation is the key to achieving this. In this text-based course, you will learn how to design and build robust RAG pipelines using Semantic Kernel and MongoDB. You will start with the core concepts of vector embeddings and semantic search, and progress to integrating these components into a cohesive application architecture. What you'll learn: Understand the foundational architecture of Retrieval-Augmented Generation systems; Configure MongoDB as a high-performance vector database for storing and querying embeddings; Implement semantic search queries to retrieve relevant contextual data; Connect language models and data stores using the Semantic Kernel orchestration framework; Apply modern prompt engineering techniques within your orchestration workflows; Practice building a complete, text-based RAG pipeline from scratch. The course begins with foundational terminology regarding vector embeddings and database indexing before guiding you through step-by-step code implementations and integration patterns. This course is designed for backend developers, software engineers, and AI enthusiasts who want to learn RAG architecture from the ground up, with no prior experience in vector databases required. Start reading today to unlock the potential of intelligent, data-driven applications.