Retrieval-Augmented Generation (RAG) | Improve the performance of large language models (LLMs)

Learn the common design patterns for LLM applications, especially the Retrieval Augmented Generation (RAG) framework. We will discuss various strategies for embedding knowledge into these models, using vector databases and knowledge graphs to fetch domain-specific data.

This discussion will not only inspire organizational leaders to reimagine their data strategies in the face of LLMs and generative AI but also empower technical architects and engineers with practical insights and methodologies.

In this video, you will learn:

What is RAG and how does it work?
How to use vector databases and knowledge graphs to enhance LLM performance
The challenges of using foundation models and how to overcome them
How to prioritize and implement LLM applications in your business

Whether you're an organizational leader, technical architect, or engineer, this video will give you the knowledge and tools you need to succeed with LLM applications.

Table of Content:

0:00 – Introduction
3:30 – What is RAG
11:00 – Vector databases & emerging technology
15:25 – Challenges of foundation models
28:00 – Prioritizing and business implications
45:00 – QnA

#retrievalgugmentedgeneration #llm #generativeai