Implementing RAG for NLP

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.