Generative AI on Microsoft Azure

Chapter 4. Retrieval-Augmented Generation

In this chapter, we will dive into one of the most impactful applications of GenAI: retrieval-augmented generation (RAG). We will examine its core principles, explore different implementation variants tailored to specific use cases, and focus on Azure-based solutions for deploying RAG effectively.

Traditional GenAI models, despite their impressive capabilities, have inherent limitations that often hinder their performance in practical, real-world scenarios. These models rely exclusively on static knowledge acquired during training, typically having a knowledge cutoff date beyond which no new information is incorporated. Consequently, they are unable to access or utilize information that emerges after training concludes. Additionally, traditional generative models are susceptible to producing plausible-sounding but incorrect information, commonly referred to as hallucinations, particularly when responding to detailed or highly specialized queries.

Addressing these limitations, RAG offers a transformative solution by integrating external knowledge sources directly into the generative process. Unlike standalone generative models, RAG dynamically retrieves relevant and current information at the moment of inference, significantly enhancing the accuracy and timeliness of the responses. By grounding outputs in real-time data, RAG effectively reduces the risk of outdated or incorrect information.

Fundamentals of RAG

The concept of retrieval ...