Fundamentals of AI Agents Using RAG and LangChain course Syllabus

Overview: This course provides a comprehensive introduction to building AI agents using Retrieval-Augmented Generation (RAG) and the LangChain framework. Designed for intermediate learners, it covers core concepts, practical implementation, and real-world applications of AI agent systems. Through hands-on projects and structured modules, learners will gain experience in integrating large language models with external data sources, managing context and memory, and automating intelligent workflows. The course spans approximately 8–12 weeks of part-time study, with an estimated total effort of 40–60 hours.

Module 1: Introduction to AI Agents & RAG

Estimated time: 8 hours

• Understanding AI agents and their role in modern applications
• Limitations of standalone large language models
• Introduction to Retrieval-Augmented Generation (RAG)
• Architecture of RAG-powered AI systems
• Real-world use cases for AI agents with knowledge retrieval

Module 2: LangChain Framework Fundamentals

Estimated time: 12 hours

• Introduction to the LangChain framework
• Connecting language models with external tools and data sources
• Building basic AI workflows using chains and prompts
• Managing agent logic and execution flow in LangChain
• Structuring intelligent systems with modular components

Module 3: Building RAG-Based AI Applications

Estimated time: 14 hours

• Integrating AI models with document databases and knowledge bases
• Implementing vector search for efficient information retrieval
• Generating accurate responses using retrieved context
• Improving response relevance and contextual awareness

Module 4: Memory, Context & Tool Integration

Estimated time: 12 hours

• Implementing short-term and long-term memory systems in AI agents
• Maintaining conversation context across interactions
• Integrating APIs and external tools for extended functionality
• Designing automated workflows powered by AI agents

Module 5: Final Project

Estimated time: 10 hours

• Design and implement a working RAG-based AI agent system
• Use LangChain to orchestrate reasoning, retrieval, and automation
• Test, evaluate, and refine agent performance

Prerequisites

• Basic programming experience in Python
• Familiarity with machine learning or AI concepts
• Understanding of large language models and NLP fundamentals

What You'll Be Able to Do After

• Build AI agents using Retrieval-Augmented Generation (RAG)
• Use LangChain to integrate language models with data and tools
• Develop context-aware AI applications with improved accuracy
• Implement vector-based retrieval systems for knowledge grounding
• Create automated workflows using intelligent agent architectures