Building Generative AI Applications with RAG and LLMs

Aspiring developers often struggle to move beyond basic chatbot prompts to build truly reliable, data-connected AI applications. This text-based course bridges that gap by teaching you how to design, build, and optimize production-ready generative AI systems from scratch. You will progress from understanding core large language model (LLM) concepts to implementing advanced patterns like Retrieval-Augmented Generation (RAG). By studying clear written explanations and analyzing practical code examples, you will gain the skills needed to connect AI models to custom data sources, control model outputs, and monitor system performance. What you'll learn: - Understand foundational generative AI concepts, model selection criteria, and cost estimation strategies. - Master reliable prompt engineering techniques and enforce structured data outputs using Pydantic. - Build end-to-end Retrieval-Augmented Generation (RAG) systems using vector databases. - Apply Parameter-Efficient Fine-Tuning (PEFT) techniques to adapt models for specific tasks. - Configure application observability and tracing tools to debug and optimize AI performance. The course starts with essential terminology and foundational AI concepts before guiding you through structured written exercises on prompt design, database integration, and system monitoring. This course is designed for software developers and tech enthusiasts who are new to AI development and want to build practical skills without needing prior machine learning experience. Start reading today to begin building your own intelligent, data-driven applications.