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Previously, we saw the various components required to build a RAG LLM system. In that article, we discussed both how a RAG system comes together and the most common services available to fill each component of that system. In this article, we’ll focus solely on using Databricks to build out RAG. Why Databricks? We’re fans of the platform. We find it one of the most complete, powerful, and useful data platforms available in the cloud.

By David Maxson
May 31, 2024

Large language models (LLMs) have introduced a whole new world of opportunities. Suddenly, the interface between machines and human communication has broken down, and ideas that were recently untenable have become almost trivially simple. It’s an exciting time, for sure. Real success, however, depends on wrapping this new technology with software that makes it truly useful. One pattern that has arisen to make that happen is Retrieval Augmented Generation (RAG).

By David Maxson
March 26, 2024

A picture is worth a thousand words - Fred R. Barnard In the rapidly changing world of LLMs, chatbots, and RAGs (Retrieval-Augmented Generation), most of the focus has been on plain text data. However, a wealth of information is available only in image form. What does something look like? How do elements of a diagram fit together? How is the food pyramid laid out? How do two algorithm outputs vary in visual quality?

By David Maxson
March 8, 2024