You can see in this example (asking about employee healthcare plans) using the base ChatGPT model, the response (in green) is not useful since it doesn’t account for the company’s own benefits policy:Īssistant helps Contoso Inc employees with their internal healthcare plan questions. When used this way, the responses you get are based on what the model has learned during training, which can be useful for general knowledge questions or an informal chat, but not what you want if you’re building an application where users should see responses based on your own data. This could be a question, a conversation turn, a pattern to extend, etc. The way you interact with large language models like ChatGPT is using natural language, giving the model a “prompt” and requesting it to complete it. We’re also releasing a GitHub repo with examples, including UX, orchestration, prompts, etc., that you can use to learn more or as a starting point for your own application. Our goal is to give you the tools necessary to build ChatGPT-powered applications starting today, using the 'gpt-35-turbo' model that's now in preview. In this blog post we’ll describe the above solution pattern, from the internals of orchestrating conversation and knowledge bases to the considerations in user experience necessary to help end users judge responses and their supporting facts appropriately.

It integrates the enterprise-grade characteristics of Azure, the ability of Cognitive Search to index, understand and retrieve the right pieces of your own data across large knowledge bases, and ChatGPT’s impressive capability for interacting in natural language to answer questions or take turns in a conversation.

The combination of Azure Cognitive Search and Azure OpenAI Service yields an effective solution for this scenario. In the context of enterprise applications, the question we hear most often is “how do I build something like ChatGPT that uses my own data as the basis for its responses?”

Users around the world are seeing potential for applying these large language models to a broad range of scenarios. The interest and excitement around this technology has been remarkable. It took less than a week for OpenAI’s ChatGPT to reach a million users, and it crossed the 100 million user mark in under two months.

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