LLMs enable new ways for users and software to collaborate. This makes them incredibly powerful, but also introduces new kinds of risks that we haven't experienced with previous software tools.

One of the biggest challenges in Graph Retrieval Augmented Generation (RAG) systems is how to communicate knowledge from the ontology (e.g., subclasses, types, property values) to the LLM. The approach described in this post is very simple and easy to reuse but surprisingly effective.

Seeds and Projects are easy and powerful tools to help ChatGPT retain context over long threads

Description of a system developed using Retrieval Augmented Generation (RAG) and a knowledge graph as a portal for Climate Obstruction researchers