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Building AI Applications and Agents with LangChain
Rating: 2.0 out of 5(1 rating)
18 students

Building AI Applications and Agents with LangChain

Develop your Generative AI Applications and Agents with LangChain
Created byMark Chen
Last updated 1/2025
English

What you'll learn

  • Learn to use LangChain to develop generative AI applications
  • Learn to use LangChain and its platforms to develop RAG applications
  • Learn to use LangChain and LangGraph to develop LLM agents
  • Learn the fundamental of LLM application development and prompting techniques

Course content

9 sections • 44 lectures • 1h 40m total length

  • Introduction to the Course2:13

    Course Introduction

    The "Becoming an AI Engineer with LangChain" course is designed to help you master the skills needed to build advanced, LLM-driven generative AI applications using the LangChain framework. It combines theoretical insights with practical, hands-on experience, making it accessible for both beginners and experienced developers looking to harness the power of LangChain to create intelligent, context-aware AI systems.

    This course is tailored for individuals who want to dive deep into the rapidly growing field of generative AI, exploring cutting-edge technologies such as Large Language Models (LLMs), prompt engineering, and integrating AI tools and platforms. With a focus on practical implementation, you'll learn to develop AI systems that are both innovative and impactful.

    What You Can Learn from This Course

    By enrolling in this course, you will gain:

    1. Foundational Knowledge:

      • An introduction to Generative AI concepts, including LLMs, Transformers, and Diffusion Models.

      • Understanding key terminologies such as prompts, context windows, tokens, and embedding vectors.

    2. Prompt Engineering Skills:

      • Techniques like zero-shot, few-shot, and Chain of Thoughts (CoT) prompting.

      • Advanced methods like Tree of Thoughts, majority voting, and self-consistency to improve model outputs.

    3. Proficiency in LangChain Framework:

      • Learn the architecture of LangChain and how to navigate its ecosystem.

      • Explore integrations for handling data stores, models, documents, agents, and tools.

    4. Model Implementation:

      • Work with Embedding Models for Text Similarities and Chat Models for Conversational AI.

    5. Data Handling and Retrieval:

      • Use document loaders for processing formats like CSV, PDF, and Markdown.

      • Implement vector data stores to manage and retrieve unstructured data effectively.

    6. Agent Development:

      • Create intelligent decision-making agents using frameworks like ReAct, Reflection, and Multi-Agent Systems.

    7. Application Development:

      • Build real-world generative AI applications like chatbots and retrieval-augmented generation (RAG) systems.

      • Understand how to streamline development and deployment processes.

    8. Best Practices and Hands-On Experience:

      • Develop practical skills with demonstrations and projects.

      • Learn tips for optimizing generative AI workflows and applications.

    Why Take This Course?

    This course offers a unique opportunity to:

    • Bridge the gap between theory and practice in generative AI.

    • Gain expertise in LangChain, a cutting-edge framework for LLM applications.

    • Build a strong foundation for a career in AI engineering with practical tools and techniques.

    Whether you're an aspiring AI engineer, a developer looking to expand your skill set, or someone curious about generative AI, this course provides the knowledge and hands-on experience to succeed in this exciting field.

  • Setting up Cloud Development Environment (CDE) with GitHub Codespace1:53
  • Python Environment Set-up for LangChain0:53
  • Setting up your OpenAI API2:24
  • Course Materials and Supplement Materials0:03
  • About the Instructor - Mark Chen0:20

Requirements

  • Have a basic understanding to Python programming
  • Have an account to OpenAI API and its API Key
  • Have an account to Anthropic API and its API Key
  • A free or premium LangSmith account

Description

Building AI Applications and Agents with LangChain


About the Course 

"Building AI Applications and Agents with LangChain" is a hands-on course designed to provide a thorough understanding of LangChain, a robust framework for developing applications with large language models (LLMs). Led by Mark Chen, founder of Mindify AI, this course is crafted to take you from the basics of generative AI to advanced LangChain components and integrations. By the end, you’ll have practical experience building applications that use LangChain to streamline data handling, model interactions, and AI deployment processes.


About the Instructor 

Mark Chen, the founder of Mindify AI, is an experienced AI engineer and entrepreneur dedicated to creating generative AI solutions. His expertise spans building LLM-driven applications, developing AI agent-based applications, and navigating the LangChain framework. Mark’s background in developing real-world AI applications gives this course a unique, practical focus that combines foundational knowledge with insights from the cutting edge of AI technology.


Course Outline 

- Chapter 1: Introduction to Generative AI and LangChain 

- Chapter 2: Working with LLMs – From Embedding to Chat Models 

- Chapter 3: Document Handling – Using Document Loaders in LangChain 

- Chapter 4: Data Storage – Vector Data Stores and Context Retrieval 

- Chapter 5: Essential Tools – LangChain Tooling and Code Integration 

- Chapter 6: Agents and Decision-Making – LangGraph Agent Applications 

- Chapter 7: LangChain on Platforms – Integrating LLMs across platforms 

- Chapter 8: Building Applications – LangChain APIs for Chatbots, RAG, and Agentic Models 


What Will You Learn from This Course 

Understand the Architecture of LangChain: Get familiar with its structure, components, and modular integrations. 

- Master Prompt Engineering: Learn zero-shot, few-shot, and chain-of-thought prompting to improve model accuracy and utility. 

- Implement Real-World Applications: Create LLM applications that handle documents, search data, and interact through custom agents. 

- Interface with AI Models: Learn how to utilize LangChain’s APIs for chat models, data stores, and agents in deployable applications.


Who Will Be Suitable for This Course 

This course is ideal for: 

- Aspiring AI Engineers and Developers who want hands-on experience with LLM-driven applications. 

- Software Engineers interested in transitioning to AI by building practical applications with a comprehensive framework. 

- Tech Enthusiasts and Researchers looking to deepen their understanding of generative AI and LangChain’s framework. 

- Anyone interested in AI development who wants to leverage the power of LLMs and AI agents to build robust, scalable applications. 


Take this course to kickstart your journey as an AI engineer and gain the skills to create real-world applications that push the boundaries of what AI can achieve.

Who this course is for:

  • People with needed to develop context-aware AI application
  • Computer science students
  • People with deep interests in generative AI
  • People who wants to become an AI engineer in the future
  • People that wants to build AI agents with LangChain and LangGraph

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