Building RAG Systems and AI Agents with Large Language Models

The ability to leverage Large Language Models (LLMs) with private data is essential for creating powerful, context-aware applications. Retrieval-Augmented Generation (RAG) is the critical pattern for achieving this in production environments today. By the end of this course, you will understand the architecture of RAG systems and multi-agent workflows. You will gain practical experience in structuring data, selecting appropriate vector stores, designing effective prompts, and deploying these AI solutions to solve specific business problems. What you'll learn: * Understand the core components and architecture of Retrieval-Augmented Generation (RAG) systems. * Practice effective prompt engineering techniques for optimizing LLM interactions and agent reasoning paths. * Apply best practices for data chunking, embedding, and indexing in a vector database. * Configure agents and multi-agent workflows using specialized low-code tools for complex task automation. * Deploy and run local LLMs using containerization fundamentals like Docker and specialized tools like Ollama. The course begins by defining LLMs, RAG, and agent concepts before moving into practical implementation steps. We walk through data preparation, vector store integration, agent configuration, and fundamental deployment strategies for making your solutions accessible. This course is designed for beginners, developers, and data practitioners who want to start building practical, context-aware AI applications. No prior experience with LLMs or advanced machine learning is required. Start building intelligent, data-driven AI systems today.