Foundations of Generative AI and LangChain

Overview

In this course, you'll build a solid foundation in generative AI, language models, and the LangChain framework, which are revolutionizing intelligent workflows. By understanding the architecture and applications of LangChain, you'll be prepared to create efficient AI solutions. This course focuses on key tools like prompts, templates, and multimodal applications. You'll gain hands-on experience with LangChain's building blocks, including LangGraph, and learn how to design AI workflows and manage memory mechanisms. What makes this course stand out is its blend of theory and real-world examples. You'll not only learn foundational concepts but also how to apply them to real-world problems using LangChain. This course is ideal for developers, AI enthusiasts, and those wanting to get a strong foothold in the world of generative AI. No prior experience with LangChain is needed, but familiarity with AI concepts is helpful. This course is part one 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

The Rise of Generative AI: From Language Models to Agents

This module explores the evolution of generative AI, focusing on the distinctions between language models and AI agents, and the practical considerations for deploying them. Learners will compare open-source and closed-source LLMs, examine licensing implications, and gain hands-on familiarity with the LangChain framework for building modular AI applications.

First Steps with LangChain

This module introduces you to the foundational concepts and practical setup required to build applications with LangChain. You'll learn how to configure API keys, explore different model providers, and understand the essential building blocks such as prompts, chains, and memory. The module also covers prompt engineering, running local versus cloud models, and the basics of multimodal AI, including image understanding.

Building Workflows with LangGraph

This module introduces LangGraph as a framework for building advanced workflows with large language models, focusing on state management, controlled output generation, and prompt engineering techniques. Learners will explore concepts such as reducers, memory mechanisms, and checkpointing to create robust, scalable applications. Practical strategies for zero-shot and few-shot prompting, as well as chain-of-thought reasoning, are also covered.