Apply ChatGPT to your own data.
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Introduction
Imagine utilizing the capabilities of ChatGPT and applying it to your own data to achieve precise control over the knowledge base for in-context and relevant responses. You can achieve this by using an approach called Retrieval-Augmented Generation (RAG). In this scenario, we combine the Azure OpenAI Service with Azure Cognitive Search to index and retrieve data of all kinds, including knowledge that is private and external to the ChatGPT large language model.
The retrieval step in Azure Cognitive Search identifies the most relevant pieces of information, even if it's millions of documents or data points, and presents the top-ranked results to the language model. This enables you to have detailed, informed interactions with your data. Since the knowledge resides outside of the ChatGPT model, you maintain control over it, and it is not used to train the model.
Equally important from an enterprise perspective, any chat session state lives entirely within your application. Whether you decide to keep it or not, and where it is stored, is fully up to you.