
Developing LLM Applications Using RAG (feat. LangChain)
jasonkang
RAG. Learn from Silicon Valley GenAI Hackathon Winner. Packed with real-world know-how.
Basic
LLM, RAG, LangChain
A basic LangChain course that extracts only the essentials from the official documentation, all for the price of a cup of coffee(?). Get familiar with LangChain syntax before diving into full-scale development!
2,343 learners
Level Basic
Course period Unlimited


Reviews from Early Learners
5.0
기깬칠
This lecture feels like having a mentor right next to you teaching you. I think the advantage is that the conversational teaching style makes it more comfortable to learn, and since I prefer this kind of interactive lecture format, it was very good for me. Also, it teaches the essential points needed for immediate implementation, so it's a great lecture for someone who needs to do something with langchain right now.
5.0
임정(지지플랏)
I've always wondered if Langchain is really necessary, but this lecture made me realize that it definitely improves the interface through features like organizing into JSON structures and chaining Runners. The easy explanations also motivated me to study further. Thank you so much for providing such an excellent lecture at an affordable price!!
5.0
김규태
How can a lecture like this be offered for just a few dollars? I wanted to create an LLM agent but felt lost because I knew absolutely nothing about LLMs, but this was a great help. Now I'm off to take the LangGraph lecture. Thank you!
LangChain
LLM
Prompt Engineering
Does LangChain feel vague and overwhelming? We'll cover just the essentials in one hour
When you try to read the official documentation, there are just countless tutorial notebooks
And even worse, it's all only in English
But once you know the basics, everything else is just application, so you don't need to follow every single tutorial
So I'll cover just the core fundamental concepts
Are they telling you not to use GPT for security reasons while asking you to build something with LLM? We'll show you how to run local models using Ollama
When writing prompts, you need to write them "in a LangChain way" to develop with a scalable structure. I'll show you how to use LangChain's PromptTemplate and ChatPromptTemplate
Do you only need a single word, but the LLM provides too much additional explanation? Are you having trouble converting and delivering responses in JSON format? We'll show you how to modify LLM-generated responses to suit your needs
Do you want to handle complex tasks smoothly? I'll show you how to develop using LangChain syntax in a readable and scalable way by utilizing LCEL syntax across multiple steps
The course was recorded in a MacOS environment, but it is also compatible with Windows and Linux as long as Python is installed.
The course uses the Cursor editor, but you can use any editor such as VSCode, Jupyter Notebook, PyCharm, etc.
Please refer to the code comments as there are no separate explanation materials such as Notion or Gitbook
Python required!
Who is this course right for?
For those curious about LangChain
People who are new to LangChain
Need to know before starting?
Python
Inflearn Verified
Career Verified
19,033
Learners
1,498
Reviews
528
Answers
4.9
Rating
10
Courses
FAANG Senior Software Engineer
(Former) GS Group AI Agent platform development/operations
(Former) GS Group DX BootCamp Mentor/Coach
(Former) FAANG Senior Software Engineer (Former) GS Group AI Agent Platform Development/Operations (Former) GS Group DX BootCamp Mentor/Coach
(Former) Tech Lead at a Series C AI Startup
Stanford University Code in Place Python Instructor
Naver Boostcamp Web/Mobile Mentor
Naver Cloud YouTube Channel presenter
Author of Building Autonomous AI Agents with LangChain & LangGraph

Wanted Pre-onboarding Frontend/Backend Challenge Instructor (6,000+ cumulative participants)
Hanghae AI Plus Course 1st Generation Coach
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7 lectures ∙ (1hr 1min)
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