RAG – Retrieval Augmented Generation

Description:

Delve into the cutting-edge world of Retrieval Augmented Generation (RAG) in this immersive half-day course. Discover how RAG seamlessly integrates information retrieval with natural language generation, revolutionizing content creation and question-answering systems. Through interactive sessions and practical exercises, you'll unlock the power of RAG and learn to leverage its capabilities for diverse applications.

Duration : Half day ( 3 Hrs)

Course Coe: BDT346

Learning Objectives:

• Understand the principles and components of Retrieval Augmented Generation (RAG).
• Learn to implement RAG models for text generation and question answering.
• Explore strategies for fine-tuning and optimizing RAG models for specific tasks.
• Gain hands-on experience through guided exercises and case studies showcasing RAG's real-world applications.

• Basic understanding of natural language processing (NLP) concepts and familiarity with Python programming language would be beneficial but not mandatory.

This course is designed for data scientists, NLP practitioners, researchers, and anyone interested in exploring advanced techniques for text generation and question answering.

This course is designed for data scientists, NLP practitioners, researchers, and anyone interested in exploring advanced techniques for text generation and question answering.

Course Outline:

• Introduction to Retrieval Augmented Generation (RAG)
• Overview of RAG architecture and components
• Key features and advantages of RAG over traditional NLP approaches
• Setting up RAG environment and libraries
• Training and fine-tuning RAG models for text generation and question answering
• Practical Applications and Case Studies
• Case studies showcasing real-world applications and use cases of RAG