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The following section explains how to enable each index, how the data flow in microsoft 365 copilot uses semantic indexing, what file types each index can handle, and how each index deals with updates. You can use a bot or a background job to query the graph api for concluded meetings owned by product leads. Today, we will focus on using copilot to generate quick conversation summaries and extract actionable insights from chat threads.

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Once indexed, all teams messages and replies are stored in azure cognitive search with vector embeddings Team members often forget to document what was discussed and agreed upon This allows you to perform semantic search or feed historical context into a copilot or internal knowledge assistant.

To write better teams messages or posts, first, make sure copilot is activated in your teams app

It may need admin permission or an app update While composing a message, use copilot's suggestions to make your communication clearer and more engaging These suggestions appear as you type. It returns precise text snippets, can layer on vector similarity search for fuzzier matches, and stitches together quick summaries to help teams answer support questions without digging through pages manually.

Ai automatically provides a summary of the conversation for agents to share with other collaborators In this article, we’ll explore how copilot in microsoft teams can help you summarize unread messages, organize action items, and get back on track without the hassle. Microsoft 365 copilot is revolutionizing how we manage information across multiple apps and communication channels One of its most powerful features is the ability to quickly summarize emails, chats, and files from specific people, saving users valuable time and helping them stay informed.

Product and engineering teams hold regular meetings to make design and feature decisions