Building AI Agents with LLMs, RAG, and Knowledge Graphs

About this course

This Specialization provides a structured pathway for building intelligent AI systems that move beyond text generation to grounded reasoning and action. Beginning with foundational concepts, learners explore how deep learning enables modern text analysis and understand the role of transformers and large language models (LLMs) as the core engine behind today’s AI systems. The second course advances into applied system design by introducing retrieval-augmented generation (RAG) and knowledge graphs. Learners develop techniques to connect language models with external data sources, improving factual accuracy and contextual understanding. Topics include building retrieval pipelines, extending agents with advanced RAG strategies, and integrating structured knowledge for richer reasoning. In the final course, learners focus on designing and orchestrating intelligent AI agents. This includes creating single- and multi-agent systems, incorporating planning and tool use, and building complete AI agent applications. The progression equips learners to integrate LLMs, retrieval systems, and knowledge structures into cohesive solutions that address complex, real-world challenges with improved reliability and depth. This specialization is based on the book Building AI Agents with LLMs, RAG, and Knowledge Graphs written by Salvatore Raieli and Gabriele Iuculano.

Course objectives

• to explain the role of language models in AI and deep learning
• to develop advanced techniques for connecting AI with external data sources
• to guide through the design process of multi-agent AI systems