What you will learn in the Build AI Agents with RAG and LangChain Course
- This course introduces the fundamentals of building AI agents using Retrieval-Augmented Generation (RAG) and the LangChain framework.
- Learners will understand how RAG enhances AI responses by retrieving relevant information from external knowledge sources.
- You will gain hands-on experience using LangChain to orchestrate AI workflows and build intelligent agent-based applications.
- The course explains how AI systems combine large language models with databases, documents, and APIs.
- Students will learn how to manage context, memory, and knowledge retrieval pipelines.
- The program focuses on building AI agents capable of reasoning, retrieving data, and automating tasks.
- By the end of the course, learners will understand how to develop AI agents that deliver more accurate and context-aware responses.
Program Overview
Introduction to AI Agents & RAG
1–2 weeks
This section introduces the core concepts of AI agents and retrieval-augmented generation.
- Understand the limitations of standalone large language models.
- Learn how RAG enhances AI accuracy using external knowledge sources.
- Explore real-world applications of RAG-powered AI systems.
- Understand the architecture of AI agents integrated with retrieval systems.
LangChain Framework Fundamentals
2–3 weeks
This section focuses on understanding the LangChain framework and how it connects language models with tools and data sources.
- Learn how LangChain connects language models with external tools and data.
- Build basic pipelines for AI-powered workflows.
- Manage prompts, chains, and agent logic.
- Understand how LangChain structures intelligent AI systems.
Building RAG-Based AI Applications
2–3 weeks
This section focuses on developing applications that combine AI models with knowledge retrieval systems.
- Connect AI models with document databases and knowledge bases.
- Implement vector search for efficient information retrieval.
- Generate accurate responses using retrieved knowledge.
- Improve response relevance and context awareness.
Memory, Context & Tool Integration
2–3 weeks
This section covers advanced features required for intelligent AI agents.
- Implement both short-term and long-term memory systems.
- Maintain conversation context across interactions.
- Integrate APIs and external tools for extended functionality.
- Design automated workflows powered by AI agents.
Final Project
1–2 weeks
In the final stage, you will build a working RAG-based AI agent system.
- Design an AI system capable of retrieving knowledge from external sources.
- Implement LangChain workflows for reasoning and automation.
- Test and refine the AI agent’s performance.
- Demonstrate practical AI application development skills.
Get certificate
Earn the Build AI Agents with RAG and LangChain Certificate upon successful completion of the course.
Job Outlook
- Skills in generative AI frameworks like LangChain and techniques like Retrieval-Augmented Generation (RAG) are in high demand.
- Companies are actively developing AI-powered applications that rely on accurate knowledge retrieval and contextual reasoning.
- Professionals with expertise in RAG pipelines, LLM orchestration, and AI agents are highly valued in modern AI teams.
- Career opportunities include roles such as AI Engineer, Machine Learning Engineer, Data Scientist, and AI Application Developer.
- Organizations deploying enterprise AI solutions increasingly rely on RAG-based architectures to improve reliability.
- Knowledge of LangChain improves opportunities in AI startups, research labs, and enterprise AI development teams.
- AI-powered knowledge systems are expected to become a core component of next-generation intelligent software products.
