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).

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?

My Summer Internship at Rearc Introduction & Background Hi, my name is Milan Patel. I am currently in 12th grade at Lenape Regional Highschool and I was a remote summer intern at Rearc from June to August 2023. AI Exploration Project General Problem Statement Given the abundance of datasets available to us, we always find it helpful to streamline idea generation. What does this dataset contain? What can I use this table for?