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Enhance LLMs using RAG and Hugging Face

BeginnerGuided Project

Learn Retrieval-Augmented Generation (RAG) by building context-aware Large Language Models (LLMs). This guided project leverages Hugging Face and Facebook AI Similarity Search (FAISS) for efficient semantic search and natural language generation, enabling personalized, context-rich responses from your own documents. Ideal for advancing your understanding of AI techniques and enhancing the capabilities of LLMs with relevant contextual information.

4.3 (13 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 74

Skills You Will Learn

  • LLM, RAG, Faiss, Python, HuggingFace

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • September 26, 2025
About this Guided Project
In the age of information overload, having the ability to retrieve the most relevant and context-aware information from vast amounts of data is invaluable. Retrieval-Augmented Generation (RAG) represents a cutting-edge technique that combines the strengths of retrieval systems and Large Language Models (LLMs) to generate high-quality, relevant responses from your own custom documents. This project is not only fascinating due to its innovative approach but also practical, as it equips you with the skills to build intelligent, responsive systems that can be applied in various domains such as customer service, content creation, and more. By completing this project, you'll gain deep insights into how state-of-the-art AI models such as BART and DPR, combined with FAISS indexing, can revolutionize document retrieval and content generation.

A look at the project ahead

Throughout this project, you will embark on a journey to master the development of a Retrieval-Augmented Generation (RAG) model, leveraging the power of Hugging Face, BART, DPR, and FAISS. Here's what you'll be able to achieve by the end of this guided project:

  • Understand and implement the Retrieval-Augmented Generation (RAG) framework, integrating Hugging Face’s BART and DPR models for robust document retrieval and response generation.
  • Gain hands-on experience in using FAISS for efficient indexing and retrieval, enabling scalable and fast semantic search within your custom document collection.

What you'll need


Before you begin this guided project, it's recommended that you have a basic understanding of Python programming and some familiarity with deep learning concepts. Experience with natural language processing (NLP) would be advantageous, but is not mandatory. You'll be working in an environment powered by IBM Skills Network Labs, which comes pre-installed with essential tools such as Python, Hugging Face libraries, and FAISS, so you can focus on learning without worrying about setting up your environment. This project is best accessed using the latest versions of Chrome, Edge, Firefox, Internet Explorer, or Safari to ensure optimal performance.

Instructors

Ashutosh Sagar

Data Scientist

I am currently a Data Scientist at IBM with a Master’s degree in Computer Science from Dalhousie University. I specialize in natural language processing, particularly in semantic similarity search, and have a strong background in working with advanced AI models and technologies.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Contributors

Victoria Nadar

Growth Marketer @IBM

Here to tell you what I learnt from my experience in the Marketing Technology Industry

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