LangChain- Develop LLM powered applications with LangChain
What you'll learn
- Become proficient in LangChain
- Have 3 end to end working LangChain based generative AI applications
- Prompt Engineering Theory: Chain of Thought, ReAct, Few Shot prompting and understand how LangChain is build under the hood
- Understand how to navigate inside the LangChain opensource codebase
- Large Language Models theory for software engineers
- LangChain: Lots of chains Chains, Agents, DocumentLoader, TextSplitter, OutputParser, Memory
- RAG, Vectorestores/ Vector Databasrs (Pinecone, FAISS)
- Model Context Protocol
Requirements
- This is not a beginner course. Basic software engineering concepts are needed
- I assume students will be familiar software engineering subjects such as: git, python, pipenv, environment variables, classes, testing and debugging
- No Machine Learning experience is needed.
Description
COURSE WAS RE-RECORDED and supports- LangChain Version 0.3+
**Ideal students are software developers / data scientists / AI/ML Engineers**
Welcome to first LangChain Udemy course - Unleashing the Power of LLM!
This course is designed to teach you how to QUICKLY harness the power the LangChain library for LLM applications.
This course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for a diverse range of topics.
Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python. I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts .
In this course, you will embark on a journey from scratch to building a real-world LLM powered application using LangChain.
We are going to do so by build 3 main applications:
Ice Breaker- LangChain agent that given a name, searches in google to find Linkedin and twitter profiles, scrape the internet for information about a name you provide and generate a couple of personalized ice breakers to kick off a conversation with the person.
Documentation Helper- Create chatbot over a python package documentation. (and over any other data you would like)
A slim version of ChatGPT Code-Interpreter
Prompt Engineering Theory Section
Introduction to LangGraph
Introduction to Model Context Protocol (MCP)
The topics covered in this course include:
LangChain
LLM + GenAI History
LLMs: Few shots prompting, Chain of Thought, ReAct prompting
Chat Models
Open Source Models
Prompts, PromptTemplates, langchainub
Output Parsers, Pydantic Output Parsers
Chains: create_retrieval_chain, create_stuff_documents_chain
Agents, Custom Agents, Python Agents, CSV Agents, Agent Routers
OpenAI Functions, Tool Calling
Tools, Toolkits
Memory
Vectorstores (Pinecone, FAISS)
RAG (Retrieval Augmentation Generation)
DocumentLoaders, TextSplitters
Streamlit (for UI)
LCEL
LangSmith
Intro to LangGraph
FireCrawl
GIST of Cursor IDE
Cursor Composter
Curser Chat
MCP - Model Context Protocol & LangChain Ecosystem
Throughout the course, you will work on hands-on exercises and real-world projects to reinforce your understanding of the concepts and techniques covered. By the end of the course, you will be proficient in using LangChain to create powerful, efficient, and versatile LLM applications for a wide array of usages.
DISCLAIMERS
Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python.
I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts.The first project of the course (Ice-Breaker) requires usage of 3rd party APIs-
Scrapin / ProxyURL, Tavily, Twitter API which are generally paid services.
All of those 3rd parties have a free tier we will use to create stub responses development and testing.
Who this course is for:
- Software Engineers that want to learn how to build Generative AI based applications with LangChain
- Developers that want to learn how to build Generative AI based applications with LangChain
- Engineers that want to learn how to build Generative AI based applications with LangChain
Instructor
- 4.6 Instructor Rating
- 37,147 Reviews
- 144,792 Students
- 9 Courses
I’m a passionate Software Engineer with years of experience in back-end development and cloud architecture. I was one of the first engineers at Orca Security, where I helped shape the company’s core technology, and today I work as a GenAI Architect at Google Cloud, helping organizations design and deploy advanced generative AI and cloud-native solutions.
I’m also proud to be a LangChain Ambassador, actively contributing to the open-source community and helping developers build powerful LLM applications using the LangChain ecosystem.
I hold a Bachelor’s degree in Computer Science from the Technion, and I’ve always had a deep passion for teaching and mentorship. I taught Functional Programming and Introduction to Computer Science at Reichman University, where I guided the next generation of software engineers.
My courses are built on real-world experience and designed to give you practical, production-ready skills—whether you’re just getting started or looking to level up.