Retrieval Augmented Generation (RAG) has become an industry standard method for interfacing with large language models by making them 'context aware'. However, there are a number of situations where a text generation problem is not solved by interacting with large vector database containing many documents. These problems require context but where the context is not known before query time and is often unrelated to existing vector stores. Usually, they are questions about single documents where desirable behavior is to allow the document to be specified at runtime.
In this accelerator, we will illustrate how to use Generative AI models to cater to Level 1 requests, allowing support teams to focus on more pressing and high visibility requests. Learning from historical communications, Generative AI Agents can maintain the same standard of support communication that the customers are used to.
This accelerator aims to provide instructions on how to build this type of system using DataRobot's generative AI solution framework. The accelerator shows how you can build a pipeline to create a knowledge base with only trusted research papers, and build a conversational agent that can answer questions from medical professionals.