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Build a RAG System for Web Data with LangChain and Llama 3.1

IntermediateGuided Project

Build a Retrieval-Augmented Generation (RAG) system for web data using LangChain and Llama 3.1-405b on watsonx.ai. In this guided project, you will set up the environment and configure LangChain to build a RAG system that generates real-time, context-aware responses from web data. This guided project is perfect for Python developers and data scientists looking to enhance their AI and language modeling skills in dynamic information retrieval.

4.7 (92 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 178

Skills You Will Learn

  • Retrieval-Augmented Generation (RAG), Llama, Python, LangChain, Artificial Intelligence, watsonx

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • October 2, 2025
About this Guided Project
Discover the power of integrating language models with retrieval systems using LangChain and Llama 3.1-405b on watsonx.ai. This guided project will teach you how to set up your environment and configure LangChain to build a Retrieval-Augmented Generation (RAG) system. By the end, you'll master generating context-aware, real-time responses from web data, showcasing the practical application of AI in dynamic information retrieval. Perfect for intermediate to advanced Python developers and data scientists eager to expand their AI and language modeling skills in a hands-on tutorial.


What you'll learn

After you complete the project, you will:

- Understand how to set up and configure LangChain for advanced language modeling tasks.
- Learn to use Llama 3.1-405b on watsonx.ai to enhance your language model's capabilities.
- Develop a Retrieval-Augmented Generation (RAG) system to generate context-aware, real-time responses from web data.

What you'll need

Prior to starting this guided project, you should have:

- Intermediate knowledge of Python programming.
- Basic understanding of natural language processing (NLP) concepts.
- Access to a current version of Chrome, Edge, Firefox, Internet Explorer, or Safari for the best experience.

Instructors

Ricky Shi

Data Scientist at IBM

Ricky Shi is a Data Scientist at IBM, specializing in deep learning, computer vision, and Large Language Models. He applies advanced machine learning and generative AI techniques to solve complex challenges across various sectors. As an enthusiastic mentor, Ricky is committed to helping colleagues and peers master technical intricacies and drive innovation.

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Contributors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

I am a data scientist and economist with a strong background in econometrics, time series analysis, causal inference, and statistics. I stand out for my ability to combine technical expertise with clear communication, turning complex data findings into practical insights for stakeholders at every level. Follow my projects to learn about data science principles, machine learning algorithms, and artificial intelligence agents.

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Hailey Quach

Data Scientist

Hi, I'm Hailey. I enjoy teaching others to build creative and impactful AI projects. By day, I’m a Data Scientist at IBM; by night, an Honors BSc student at Concordia University in Montreal, always exploring new ways to combine learning with innovation.

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