Becoming An Ai Engineer With Langchain

Develop your Generative AI Application with LangChain

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

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

Becoming an AI Engineer with LangChainAbout the Course  "Becoming an AI Engineer 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.  - Build and Deploy 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.

Overview

Section 1: Part 0 - Course Introduction / Overview

Lecture 1 Introduction to the Course

Lecture 2 Setting up Cloud Development Environment (CDE) with GitHub Codespace

Lecture 3 Python Environment Set-up for LangChain

Lecture 4 Setting up your OpenAI API

Lecture 5 Course Materials and Supplement Materials

Lecture 6 About the Instructor - Mark Chen

Section 2: Part 1 - Introduction to Generative AI and LangChain

Lecture 7 What is Generative AI?

Lecture 8 What is Large-Language Model (LLM)?

Lecture 9 What is Prompt Engineering?

Lecture 10 What is LangChain?

Lecture 11 Section 1 Summary

Section 3: Part 2 - Chat and Embedding Models

Lecture 12 OpenAI Embedding Models

Lecture 13 OpenAI Chat Models

Lecture 14 Anthropic Chat Models

Lecture 15 Section 2 Summary

Section 4: Part 3 - Documents and Loaders

Lecture 16 LangChain Document and Document Loaders - PDF

Lecture 17 LangChain Markdown Loader

Lecture 18 LangChain HTML Loader

Lecture 19 LangChain JSON and CSV Loaders

Lecture 20 Section 3 Summary

Section 5: Part 4 - Data Stores

Lecture 21 Introduction to Embedding and Vector Search

Lecture 22 Chroma Vector Store

Lecture 23 Pg-Vector Vector Store

Lecture 24 Milvus Vector Store

Lecture 25 Section 4 Summary

Section 6: Part 5 - Tools

Lecture 26 Brave Search

Lecture 27 Rize.io Code Interpreter

Lecture 28 Bash Shell

Lecture 29 Section 5 Summary

Section 7: Part 6 - Agents

Lecture 30 Introduction to LLM and AI Agents

Lecture 31 Agent Architecture - ReAct

Lecture 32 Agent Architecture - Reflection

Lecture 33 Agent Architecture - Plan and Solve (Execute)

Lecture 34 Agent Architecture - Multi-agent System

Lecture 35 Section 6 Summary

Section 8: Part 7 - Platforms

Lecture 36 LLM Application Observability and Evaluation

Lecture 37 Introduction to LangSmith and Tracing

Lecture 38 Introduction to LangChain Chat

Lecture 39 Section 7 Summary

Section 9: Part 8 - Applications / Summary

Lecture 40 Introduction to Context-aware AI Applications

Lecture 41 Naive Retrieval-Augmented Generation (RAG) Application

Lecture 42 Agentic Retrieval-Augmented Generation (RAG) Application

Lecture 43 Course Summary

Lecture 44 Future Learning

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