Enhancing LLMs with Retrieval Augmented Generation (RAG) 

In this proof of concept, we explore the impressive capabilities and limitations of large language models like GPT-3 and GPT-4. We discuss their performance when interacting with familiar subjects as well as their behavior when presented with unfamiliar topics and information beyond their training data.
We demonstrate an innovative approach to enhancing the models' interaction with large amounts of data by using the Clarifai application and its capabilities to break down extensive documents into manageable chunks and generate meaningful embeddings. We further explain how these embeddings are used to find relevant answers to queries.
Using comprehensive documents from The International Crisis Group, we give an example of how this process works, explaining how the system deals with a query about terrorism and a specific individual who isn't part of the GPT-3 training data. We also showcase how the system can extract geographical locations and plot them on a map, demonstrating its potential in the field of geospatial intelligence.
By the end of this video, you'll understand the transformative potential of integrating large language models with advanced data handling and geospatial intelligence tools. Watch and discover how we are pushing the boundaries of artificial intelligence.
This functionality will be released in an upcoming version of Clarifai shortly.