Cloud Storage Considerations for Retrieval Augmented Generation (RAG) in AI Applications

Tue Sep 17 | 4:05pm
Location:
Cypress
Abstract

Data enhances foundational LLMs (e.g. GPT-4, Mistral Large and Llama 2) for context-aware outputs. In this session, we cover using unstructured, multi-modal data (e.g. PDFs, images or videos) in retrieval augmented generation (RAG) systems. Learn about how cloud object storage can be an ideal file system for Andrej Karpathy's LLM-OS concept including the transformation and use of domain-specific data, storing user context and much more.

Learning Objectives

Explain why domain-specific data and the RAG pattern is so important to building customized AI applications for different industries and domains.
Understand the LLM OS concept including the notion of a filesystem for storing domain-specific data, user context, etc. and why cloud object storage is an ideal storage system for this filesystem.
Use cloud object storage in conjunction with a vector database to build sample RAG applications.

Abstract

Data enhances foundational LLMs (e.g. GPT-4, Mistral Large and Llama 2) for context-aware outputs. In this session, we cover using unstructured, multi-modal data (e.g. PDFs, images or videos) in retrieval augmented generation (RAG) systems. Learn about how cloud object storage can be an ideal file system for Andrej Karpathy's LLM-OS concept including the transformation and use of domain-specific data, storing user context and much more.

Learning Objectives

Explain why domain-specific data and the RAG pattern is so important to building customized AI applications for different industries and domains.
Understand the LLM OS concept including the notion of a filesystem for storing domain-specific data, user context, etc. and why cloud object storage is an ideal storage system for this filesystem.
Use cloud object storage in conjunction with a vector database to build sample RAG applications.


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