Retrieval Augmented Generation (RAG) Introduction (RXM403) Course Overview

Target Audience for Retrieval Augmented Generation (RAG) Introduction (RXM403)

Retrieval Augmented Generation (RAG) Introduction (RXM403) equips IT professionals with the knowledge to leverage RAG techniques for enhanced data retrieval and generation in AI applications.

• Data Scientists
• Machine Learning Engineers
• AI Researchers
• Business Intelligence Analysts
• Software Developers
• Content Strategists
• IT Trainers
• Solution Architects
• Research Analysts
• Technical Project Managers
• Technical Writers
• Product Managers
• Digital Marketing Professionals
• Data Analysts

Learning Objectives - What you will Learn in this Retrieval Augmented Generation (RAG) Introduction (RXM403)?

Course Introduction

The Retrieval Augmented Generation (RAG) Introduction (RXM403) course equips students with foundational knowledge in RAG systems, focusing on the integration of retrieval mechanisms with generative models for enhanced data processing and content generation.

Learning Objectives and Outcomes

• Understand the core concepts of Retrieval Augmented Generation (RAG).
• Learn about the integration of retrieval techniques with generative models.
• Explore the benefits of RAG in improving content accuracy and relevance.
• Gain insights into various use cases and applications of RAG.
• Study the underlying algorithms that power RAG systems.
• Develop skills in implementing RAG solutions in real-world scenarios.
• Analyze performance metrics relevant to RAG applications.
• Investigate challenges and limitations associated with RAG techniques.
• Explore the future trends and advancements in RAG technology.
• Build a foundational understanding of AI ethics in RAG deployment.