Generative AI Engineering: LLMs, RAG, and Agentic Systems

The Generative AI Engineering: LLMs, RAG, and Agentic Systems course, created by seasoned instructor Rajeev Sakhuja, represents an exhaustive deep dive designed to transform software engineers, developers, and system architects into highly skilled Generative AI experts. This extensive 28.5-hour curriculum bridges the gap between basic API querying and advanced production-grade AI system architecture. Learners will explore the internal mechanics of Large Language Models (LLMs) and master state-of-the-art frameworks like LangChain and LangGraph for designing intricate multi-agent systems. The program places heavy emphasis on building high-performance Retrieval-Augmented Generation (RAG) pipelines, utilizing modern embedding models, vector databases, reranking strategies, and advanced search patterns to mitigate hallucinations. Beyond theoretical concepts, students will gain hands-on experience in implementing Model Context Protocol (MCP) servers, allowing LLMs to interact seamlessly with external databases and tools. Additionally, the course tackles practical model optimization, walking developers through fine-tuning workflows using Hugging Face datasets and quantization techniques to balance computational cost and latency. By focusing on production readiness, including human-in-the-loop workflows, system evaluation, structured outputs, and scaling strategies, this training is ideal for tech professionals who want to build, deploy, and maintain robust, autonomous agentic systems in enterprise environments.