How Copilot and Intelligent Recap are Aggregating Value for Microsoft Teams’ Users
Meetings are an essential part of our work, where we share ideas, make decisions, and collaborate with others. However, meetings can also be challenging, especially when we have to deal with multiple agendas, diverse perspectives, and complex topics. How can we make our meetings more productive, engaging, and accessible? How can we capture the key insights and outcomes of our meetings, and act on them effectively? How can we leverage the power of artificial intelligence (AI) to enhance our meeting experience and productivity? In this blog post, we will introduce you to two new AI-powered features in Microsoft Teams that aim to answer these questions: Intelligent Recap and Microsoft M365 Copilot.Bringing OpenAI into an Outlook add-in: a business mail generator
The tech world has always shown a lot of interest in Artificial Intelligence, especially in the last years. However, recently, interest has started to spread also outside the tech enthusiast bubble. Dall-E, the model developed by OpenAI to create images, started to give a new meaning to "generative AI", showing the capabilities of these new powerful AI models. In this blog post we'll combine the best of both worlds: the productivity offered by the Microsoft 365 ecosystem and the content generation capabilities of the latest AI models. We're going to build an Outlook add-in that, through OpenAI APIs will help people to craft professional business mails from one or two sentencesBring the ChatGPT model into our applications
In the first two posts of this series we have developed an Outlook add-in that, using the Open AI models, helps users to generate professional business mails starting from a simple sentence. In the first post, we achieved this goal by directly using the OpenAI APIs; in the second one, instead, we have leveraged the Azure OpenAI service. In both cases, however, we didn't use the model that powers up ChatGPT, because it wasn't available yet at the time of writing, but we leveraged a similar one called Davinci. Since a few days, both OpenAI and Azure OpenAI have finally made available the ChatGPT model, so that developers can integrate it into their experiences. In the previous posts, we said that the when this would have happened, we wouldn't have needed to change much code, probably just the model's name. This turned out to be partially true: the overall architecture of the add-in is unchanged, but the way you provide the prompt to the ChatGPT model is slightly different than the way you do it with the Davinci one. The different approach comes from the fact that, while Davinci was created mainly for content generation scenarios, ChatGPT targets specifically conversational ones. As such, you must be able to send entire conversations to the API, so that it can gather the required context to handle a conversation without having to specify all the information at each prompt. In this post, we're going to learn the changes we must make based on the service we're using.
