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Generative AI (GenAI) is the application of machine learning algorithms to large data sets to generate content, such as text, images, audio, and video. Popular GenAI tools include ChatGPT, DALL-E, Bard, and Midjourney.

Retrieval-augmented generation optimizes the output of large language models (LLMs) by consulting an authoritative knowledge base outside of its original training data before generating a response, effectively extending its capabilities beyond its original training data. RAG is a cost-effective way to improve LLM outputs for specific domains, by leveraging an internal database of proprietary, custom data.

The most important attribute for AI storage is performance agility. AI storage must have the ability to grow from a few hundred gigabytes to petabytes, perform well with rapidly changing mixed workloads, serve data to training and production clients simultaneously throughout a project’s life, and support the data models used by project tools. The additional attributes of an ideal AI storage solution are: capacity flexibility, availability and data durability, space and power efficiency, support for data models, security, and operational simplicity.