Overview

This course introduces the concepts, tools, and practical techniques behind LangChain, the leading framework for building intelligent applications powered by Large Language Models (LLMs). It blends conceptual understanding with hands-on implementation to help you design, build, and deploy context-aware, tool-using AI systems. Whether you’re a developer, data scientist, or AI practitioner, this course provides a clear roadmap for transforming LLMs into dynamic, reasoning-driven applications that interact with real-world data and APIs. Through guided lessons, structured demonstrations, and project-based learning, you’ll explore how LangChain connects prompts, models, memory, and tools into composable workflows. You’ll learn to build Retrieval-Augmented Generation (RAG) pipelines, integrate LangServe for deployment, and implement LangSmith for observability and evaluation. The course culminates with a capstone Knowledge Assistant project, where you’ll combine RAG, multi-agent systems, and secure API integrations into a fully functional, deployable AI assistant. By the end of this course, you will be able to: • Understand the architecture and components of LangChain for LLM development. • Build multi-step reasoning pipelines and retrieval-augmented generation (RAG) workflows. • Implement memory, tools, and agents to enable contextual, goal-oriented behavior. • Evaluate and optimize LLM applications for performance, safety, and scalability. This course is ideal for AI developers, data scientists, and software engineers seeking to go beyond prompt-based experimentation and build real-world, production-ready LLM applications. A working knowledge of Python and APIs is recommended, but the course provides guided support to help learners of all backgrounds master the LangChain ecosystem. Join us to master the framework that powers today’s most advanced generative AI applications.

Syllabus

LangChain Fundamentals

Learn the foundations of LangChain and its Expression Language (LCEL) for building modular, composable LLM workflows. This module covers core components such as prompt templates, memory, and chain composition, enabling learners to design structured reasoning pipelines and create their first multi-step LLM chain.

Building Context-Aware Applications - RAG and Document Pipelines

Explore Retrieval-Augmented Generation (RAG) to connect LLMs with external knowledge sources. Learners will build document ingestion and validation pipelines, create embeddings, and evaluate retrieval workflows using LangSmith. By the end, you’ll construct a retrieval-based Q&A system powered by LangChain.

Connecting Agents and Tools

Discover how to build dynamic, decision-making AI systems using LangChain agents and LangServe. This module focuses on creating tool-using agents, integrating secure APIs, and deploying workflows as production-ready services. Learners will complete the capstone Knowledge Assistant, combining chains, RAG, and multi-agent communication protocols.

Course Wrap-Up and Assessment

Deploy, refine, and optimize your multi-agent Knowledge Assistant for real-world use. This module emphasizes fine-tuning, performance monitoring, and best practices for scalable LangServe deployments. Learners reflect on their project, review key takeaways, and prepare for advanced experimentation with custom and fine-tuned LLMs.