Let’s explore some of the foundational elements that help power Microsoft 365 Copilot. By delving into these core components, you can gain a clearer understanding of the intricate processes that enable Copilot to offer its insightful recommendations and suggestions.
Large language models
Large language models (LLMs) represent a class of artificial intelligence models that specialize in understanding and generating human-like text. The “large” in LLM signifies both the size of the models in terms of the number of parameters they encompass, and the vast volume of data on which they’re trained. LLMs, including models like ChatGPT, are a type of generative AI. Instead of merely predicting or classifying, generative AI, like LLMs, can produce entirely new content. When applied to text, LLMs can generate contextually relevant and syntactically correct responses based on the provided prompts.
In the context of Microsoft 365 Copilot, LLMs are the engine that drives Microsoft 365 Copilot’s capabilities. Microsoft’s Azure OpenAI Service privately hosts these models, which Microsoft 365 Copilot uses to understand user inputs and generate relevant responses. Through the careful application of these models, Microsoft 365 Copilot helps you navigate your work more effectively, while ensuring privacy and data integrity.
Microsoft 365 keeps your data logically isolated by tenant. This design, together with encryption, ensures privacy while processing and at rest.
Natural language processing
Natural language processing (NLP) is a pivotal AI technology that helps machines understand, interpret, and respond to human language in a way that’s meaningful. In essence, NLP is the technology behind Copilot’s ability to read, comprehend, and generate text similar to how humans would. Some of the components involved are:
- Tokenization. Simplifies complex paragraphs by breaking down text into smaller chunks, like words or phrases.
- Semantic Analysis. Helps Copilot understand the underlying meaning or context.
- Sentiment Analysis. Assess the mood or emotion behind a text, Copilot can understand user intent more accurately.
- Language Translation. Aids in multilingual tasks, allowing Copilot to assist users across different languages.
NLP is integral to Microsoft 365 Copilot. It bridges the gap between human language and machine understanding. This technology ensures that when you ask Copilot something, it understands and responds effectively.
Microsoft Graph
Microsoft Graph serves as the connective tissue that integrates all your Microsoft 365 services and data. Microsoft 365 Copilot applies Microsoft Graph to synthesize and search content from multiple sources within your tenant. The Microsoft Graph API brings more context from user signals into the prompt, such as information from emails, chats, documents, and meetings. This information includes data from services like Outlook, OneDrive, SharePoint, Teams, and more.
Microsoft Graph brings this information together so that users don’t need to navigate away or switch apps. It enables Microsoft 365 Copilot to bring the relevant information to you. When doing so, Microsoft 365 Copilot takes into account Microsoft 365 user permissions, data security, and compliance policies. It only generates responses based on the information the user has permission to access.
Important
Prompts, responses, and data accessed through Microsoft Graph aren’t used to train foundation LLMs, including those used by Microsoft 365 Copilot.
Microsoft 365 apps
Apps such as Word, Excel, PowerPoint, Outlook, Teams, Loop, and any newly integrated apps operate with Copilot to support users in the context of their work. For example, Copilot in Word specifically assists users in the process of creating, comprehending, and editing documents. In a similar way, Copilot in the other apps helps users in the context of their work within those apps.
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