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Project: Generative AI Applications with RAG and LangChain

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IntermediateCourse

Unlocking the power of Generative AI, this course empowers learners with Intermediate skills in both RAG and LangChain. Participants will explore innovative AI applications, enhancing their proficiency in Genera. Through hands-on projects, the course fosters a practical understanding of AI's capabilities and applications. Ideal for those seeking to deepen their AI expertise, the course bridges theory and practical implementation.

4.8 (225 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Information Technology

Enrollment Count

  • 25.20K

Skills You Will Learn

  • Generative AI, Gradio, LangChain, RAG, Vector Database

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 3 weeks, 2-4 hr

Platform

  • Coursera

Last Update

  • March 17, 2026
About this Course
Welcome to the Project: Generative AI Applications with RAG and LangChain course. We are thrilled to have you embark on this advanced journey to apply your knowledge and skills in Generative AI, focusing on Retrieval-Augmented Generation (RAG) and LangChain. This capstone project will allow you to build practical applications that harness the power of these technologies.
Prerequisites
To get the most out of this course, you should be comfortable with the following topics and technologies:
  • Basic understanding of generative AI concepts and models.
  • Familiarity with Python programming, particularly in AI/ML contexts.
  • Experience with libraries such as LangChain and frameworks for document processing and retrieval.
If you need to learn more about these topics before taking this course, the following courses will offer the experience you need for success:
After completing this course, you will be able to:
  • Use LangChain to load documents from various sources such as PDF, CSV, URLs, and text. 
  • Apply text-splitting techniques with RAG and LangChain to enhance model responsiveness. 
  • Create and configure a vector database to store document embeddings and then develop a retriever to fetch document segments based on queries. 
  • Set up a simple Gradio interface for model interaction and construct a QA bot using LangChain and LLM to answer questions from loaded documents.
Course Outline
This course consists of three comprehensive modules:
Module 1: Document Loader using LangChain
  • Description: Learn to load documents from various sources and apply text-splitting techniques to enhance model responsiveness.
  • Key topics:
  • Different document loaders from LangChain
  • Text-splitter strategies
Module 2: RAG using LangChain
  • Description: Create and configure a vector database, develop a retriever for document segments, and embed documents using watsonx’s embedding model.
  • Key topics:
  • Embedding the document
  • Advanced retrievers in LangChain
Module 3: Create a QA Bot to Read Your Document
  • Description: Set up a Gradio interface for model interaction and construct a QA bot to answer questions based on loaded documents.
  • Key topics:
  • Introduction to Gradio
  • Wrapping the model into a QA bot
Tools/Software
In this course, you will use free versions or trials of several tools including:
  • LangChain
  • watsonx
  • Gradio
Disclaimer: Some activities in this course involve using paid services/models in watsonx. These activities are optional, and you may incur additional costs if you choose to use them.
Tips for success
  • Stay organized and keep track of deadlines.
  • Regularly practice using the tools and technologies discussed in the course.
  • Engage with the community and ask questions if you need help.
  • Experiment with the examples provided to deepen your understanding.
Congratulations on taking this important step toward mastering Generative AI applications! Enjoy your learning journey!