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Implementing RAG for NLP

Course Summary

Implementing RAG for NLP is designed for software developers and data scientists who are familiar with NLP and Generative AI, aiming to deepen their understanding and practical skills in Retrieval-Augmented Generation (RAG). Participants will explore the essentials of RAG, its implementation using TensorFlow, Keras, and Hugging Face, and evaluate it against traditional fine-tuning methods. Through a blend of theory and hands-on labs, attendees will learn to enhance their language models effectively for various NLP tasks.

Learn to enhance language models for various NLP tasks through the use of RAG.
Anyone with a foundational understanding of Python, NLP, Deep Learning, and Hugging Face (optioonal).
Software Developer | Data Scientist
Skill level
Lecture | Case Studies | Labs | Hackathon
2 days
Related technologies
Python | NLP | Hugging Face | Tensorflow | Keras


Learning objectives
  • Understand the fundamentals and architecture of RAG.
  • Implement RAG in TensorFlow and integrate it with Hugging Face for various NLP tasks.
  • Compare the practical aspects of using RAG versus traditional fine-tuning methods in terms of coding complexity, training time, and performance.
  • Optimize RAG models for real-world applications and prepare them for deployment.


What you'll learn:

In this course, you'll learn:
  • Foundations and Practical Applications
  • Quick Recap of Tools
    • Brief overview of TensorFlow, Keras, and optionally Hugging Face
    • Setting up the environment directly in Google Colab
  • Introduction to RAG
    • Understanding the concept and architecture of Retrieval-Augmented Generation
    • Discussing the relevance and advantages of RAG in NLP
  • Basic Implementation of RAG
    • Lab: Building a simple RAG model for a specific task (e.g., summarization)
    • Step-by-step guide through the coding process and integration with Hugging Face
  • Applying RAG in Practical Scenarios
    • Lab: Utilizing RAG on a pre-built application, such as a knowledge-based QA system
    • Customizing the application to suit different NLP tasks
  • Advanced Techniques and Comparative Analysis
  • RAG for Advanced NLP Challenges
    • Lab: Implementing RAG for a complex task like interactive chatbots
    • Adjusting and optimizing the retrieval component for better performance
  • Comparing RAG with Fine-Tuning
    • Lab: Assessing coding complexity, training time, and performance between RAG and fine-tuned models
    • Detailed comparison and practical implementation tips
  • Model Optimization and Best Practices
    • Identifying which models benefit more from RAG, based on their architecture
    • Best practices for deploying RAG models in production environments
  • Final Hackathon
  • Topic: Optimizing RAG for Real-Time Question Answering Systems (Using the SQuAD dataset)
    • Participants will work on enhancing a RAG model to optimize its efficiency and accuracy in real-time question answering, applying the skills learned throughout the course.


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