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Developing LLM Applications Using RAG (feat. LangChain)

RAG. Learn from Silicon Valley GenAI Hackathon Winner. Packed with real-world know-how.

(4.9) 243 reviews

2,372 learners

  • jasonkang
이론 실습 모두
NLP
LLM
RAG
LangChain
vector-database
openAI API

Reviews from Early Learners

What you will learn!

  • LangChain

  • Large Language Model

  • Vector Database

  • Retrieval Augmented Generation(RAG)


RAG Crafted by a Silicon Valley GenAI Hackathon Winner

  • Data Preprocessing and Efficient Retrieval: Learn data preprocessing techniques necessary for RAG configuration and methods to maximize search efficiency using keywords.

  • Efficient Prompt Writing Methods: With improved LLM performance, you can now achieve good results even when writing prompts in Korean. Learn how to write Korean prompts using LangChain's PromptTemplate.

  • LLM Performance Evaluation and Service Optimization: Learn how to systematically measure and optimize model performance, reliability, and accuracy through LLM evaluation after service deployment.

RAG? Retrieval-Augmented Generation?

RAG is Retrieval Augmented Generation, a technology that enhances the performance of Large Language Models (LLMs). LLMs have excellent language understanding and generation capabilities by learning from vast amounts of text data, but they have limitations such as bias and factual errors. RAG can complement these limitations through real-time information retrieval and improve accuracy and reliability.

Features of This Course

📌 This contains the know-how learned through hands-on experience while developing, deploying, and operating LLM Applications in real-world business environments

📌 10% theory, 90% practice. Only essential theory is explained briefly, and all lectures consist of live coding

📌 I intentionally did not edit the errors. You can learn how to debug while developing LLM services.

📌 100% lecture question resolution! We solve difficulties encountered in lecture content or real-world work together through Q&A

I recommend this for people like this

I don't know where to start.
Developers/development teams who want to create services using LLM, but feel
overwhelmed about where to begin

What is RAG?
I'm curious about what RAG is and why it's important. For those who want to understand the latest technology and use it to develop their own AI applications.

What should we do next?
Developers/Development teams
who need to solve
Hallucination issues during LLM Application development

After taking the course

  • Data Preprocessing and Keyword Utilization: You can learn data preprocessing techniques necessary for RAG configuration and methods to maximize search efficiency by utilizing keywords.

  • Model Performance Evaluation: Through LLM evaluation, you'll learn how to systematically measure and optimize a model's performance, reliability, and accuracy. You'll learn how to improve model quality through evaluation results.

  • Service Deployment and Maintenance: You'll learn how to deploy and maintain applications using tools like Streamlit, and acquire skills that can be immediately applied in practical work.

  • Solving Hallucination Problems: You'll learn techniques to minimize inaccurate information generated by LLM models and provide more reliable information.

  • Understanding and Applying the Latest AI Technologies: You can understand cutting-edge AI technologies like RAG and use them to develop your own AI applications

You'll learn this content.

LLM Response Streaming

If users keep seeing
a loading screen while the LLM is generating responses, it will feel like the service is slow. Learn how to
improve user experience through streaming.

01j11jq3cd7cfchsg98bz98x3z

Providing Sources for Answers

Hallucination, the biggest problem in LLM services.
Learn how to improve the reliability of answers by providing users with the sources of answers
while generating responses

01j11jt3pppzy0k3edef9xr95b

LLM Evaluation Using LangSmith

During service operation, the Knowledge Base also changes,
and prompts need to be modified as well. Every time there's an update,
developers can't test each one individually.
For stable service operation, learn how to verify model accuracy using LangSmith

01j11k0240qbeh71ncjsxvdgw1

LangChain Expression Language (LCEL)

Did you know that LangChain can be used by connecting various chains together? Using LCEL syntax,
you'll learn how to implement and connect chains with different functionalities

01j1fhrv5ssq8t0emrfr8f44r7

Vector Database(Chroma, Pinecone)

Using LangChain with Vector Databases like Chroma and Pinecone
to store data and retrieve relevant documents through similarity search

The person who created this course



Pre-enrollment Reference Information

Practice Environment

  • The course is explained based on MacOS.

    • If Python runs on Windows and Linux environments, you can follow along.


Learning Materials

  • Source Code GitHub Repository (Jupyter Notebook, Streamlit)

  • Supplementary GitBook for Additional Explanations

Prerequisites and Important Notes

  • Python Basic Syntax

  • Anyone who has used ChatGPT even once will be able to understand this easily.

  • This would be most helpful for those who have experienced difficulties while developing LLM Applications.

Recommended for
these people

Who is this course right for?

  • A developer looking to build an LLM service

  • Developer with LLM Application development experience

  • Developer struggling with RAG setup

Need to know before starting?

  • Python

Hello
This is

11,707

Learners

589

Reviews

343

Answers

4.9

Rating

9

Courses

Curriculum

All

25 lectures ∙ (3hr 36min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

243 reviews

4.9

243 reviews

  • hyuntaklee님의 프로필 이미지
    hyuntaklee

    Reviews 1

    ∙

    Average Rating 5.0

    5

    100% enrolled

    There is no such great lecture in Langchain! I bought all the Langchain-related books on the market to try to learn Langchain on my own, and I also bought a lot of lectures, but there were more than one or two that became useless because of Langchain version updates, and there was no explanation that was easy for a beginner to understand. However, Kang Byeong-jin's lectures are all the latest versions, and as you listen, you can understand each and every logic. This is a lecture that I would like to highly recommend to anyone who wants to learn Langchain.

    • jasonkang
      Instructor

      Thank you! I think the advantage of the lecture is that it can be updated additionally when the code is deprecated. If there is an update in the langchain, I will update the lecture and let you know~

  • swgoodcode님의 프로필 이미지
    swgoodcode

    Reviews 9

    ∙

    Average Rating 5.0

    5

    95% enrolled

    Impressions 1) I created a personal llm service? Real story?… Easy… 2) How did the instructor learn all these skills… Will I be able to continue? Frustrated… But suddenly an idea came to me… Let’s urge the instructor to create the next lecture… I’ll wait! Please hurry up and give me the next lecture!! Since the internet is not available in my work, I’m curious about how to do it with Rama3. And I want to cover Langsmith well.

    • jasonkang
      Instructor

      I think I have been recognized as being "able to take the lecture and implement the service". Thank you very much. As I answered the question you posted, LLM Evaluation will film a separate lecture. I will let you know first when the lecture is released!

  • host08060121님의 프로필 이미지
    host08060121

    Reviews 1

    ∙

    Average Rating 5.0

    5

    95% enrolled

    I was able to learn the development process in a really easy-to-understand way, which made the days of struggling with ChatGPT to create a simple chatbot seem meaningless. It was so great to be able to know what was needed where, unlike when I studied alone. Even Windows users could follow along without much difficulty. I recommend it.

    • jasonkang
      Instructor

      Thank you so much ☺️ I planned it so that even beginners can easily follow along, and I think it was well-received! I will continue to work on LLM-related tasks in the future, so I will share a lot of materials that can be helpful!

  • guinnessop5968님의 프로필 이미지
    guinnessop5968

    Reviews 7

    ∙

    Average Rating 5.0

    5

    69% enrolled

    It's a short and powerful lecture. It delivers only the essential points. After listening to other lectures, they just made it more difficult by explaining in circles, and I couldn't understand them. 😭😭😭😭😭😭 Please make more lectures.

    • buildup님의 프로필 이미지
      buildup

      Reviews 6

      ∙

      Average Rating 5.0

      Edited

      5

      100% enrolled

      The lecture is very fun and dynamic. And it's the best because it covers most of the things I wanted to know and was curious about. I've spent quite a bit on Inflearn, but this is the first time I've watched a lecture over and over again. I'm definitely getting a grasp of the concepts. Thank you so much. I highly recommend it.

      $51.70

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