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Intelligent Retrieval and Agent Architectures with LangChain

Packt via Coursera

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Overview

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In this course, you'll master the design and implementation of retrieval-augmented generation (RAG) systems and intelligent agents using LangChain. You’ll explore key techniques such as advanced vector indexing, document chunking, and the creation of custom tools to build scalable AI solutions. Through practical examples, you will learn to deploy sophisticated, production-ready AI systems that can solve complex, real-world tasks. The course focuses on constructing intelligent agents and advanced multi-agent systems, ensuring your applications are both reliable and scalable. What makes this course unique is its combination of theoretical knowledge with practical, hands-on learning. You will engage with real-world scenarios and detailed walkthroughs that prepare you to deploy AI-driven solutions confidently. This course is ideal for developers, AI professionals, and engineers looking to build intelligent systems. Familiarity with software development and AI concepts is recommended. This course is part two of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Syllabus

Building Intelligent RAG Systems

This module explores how Retrieval-Augmented Generation (RAG) systems enhance language models by integrating external knowledge sources. Learners will examine the architecture and components of RAG pipelines, including vector indexing, document chunking, and advanced retrieval techniques. Practical applications, such as building a corporate documentation chatbot, are also covered.

Building Intelligent Agents

This module explores how to build intelligent agentic applications using large language models (LLMs) and LangChain. Learners will discover how to integrate, define, and customize tools, handle errors, and leverage advanced tool-calling strategies to enable agents to interact with the world and solve complex tasks. Practical examples and best practices for planning, workflow management, and robust agent design are included.

Advanced Applications and Multi-Agent Systems

This module explores advanced techniques for designing and orchestrating intelligent agents, including multi-agent collaboration, consensus mechanisms, and reasoning path decomposition. Learners will gain hands-on experience with frameworks like LangGraph and LangChain, and discover how algorithms such as Monte Carlo Tree Search can optimize agent decision-making. By the end, you'll understand how to build, manage, and deploy sophisticated agentic applications.