RAG explained in 60 seconds: https://youtu.be/bN9oGxZCIU8?si=1f9Ax3wbr3sFFyxq
Over the past few weeks, a number of people have asked me about Retrieval Augmented Generation (RAG).
Here is a brief overview of RAG and how it enhances LLMs.
TL;DR:
RAG is a framework that upgrades Large Language Models (LLMs) by integrating real-time, enterprise-specific data. This approach keeps AI outputs up-to-date, relevant, and more aligned with business needs, addressing the typical knowledge cutoff of LLMs.
Deep Dive:
RAG represents a significant shift in LLM application, enabling these models to dynamically access and utilize updated external data. This method not only keeps the AI's responses current but also tailors them to specific business contexts.
Key Advantages of RAG:
1. Addresses Knowledge Cutoff: RAG allows LLMs to access fresh data beyond their training set, ensuring timely and relevant outputs.
2. Customizable AI Responses: By connecting to specific enterprise databases or documents, RAG delivers insights that are directly applicable to the business.
3. Efficiency in Updating Models: It offers a practical alternative to frequent model retraining, saving time and resources.
How RAG Works:
RAG combines the generative power of LLMs with a retrieval function. This function searches connected data sources for information relevant to a given query, which the LLM then uses to generate informed and current responses.
Applications in Business:
Practical uses of RAG include enhancing customer service bots with the latest product data or equipping financial analysis tools with real-time market information, making AI systems more responsive and effective in a business setting.
Image Source: Generative AI with Large Language Models (DeepLearning.AI)
