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Monitoring is the process of collecting system data and metrics that determine if a system is healthy and operating as expected. Monitoring exposes errors that occurred and when they happened. To investigate issues and remediate errors, historical data is analyzed to get a picture of the health of a system or process.

Monitoring Fabric activities

In Fabric, you schedule activities and jobs that perform tasks like data movement, and transformation. Activities have dependencies on one another. You need to make sure that data arrives in its expected location on time and that system errors or delays don’t affect users or downstream activities. End-to-end processes need to be managed to ensure they’re reliable, performant, and resilient. One aspect of this monitoring is identifying and handling long-running operations and errors effectively. By doing this, you can minimize downtime and quickly address any underlying issues.

The following activities in Fabric allow you to perform tasks that deliver data to users. These activities should be monitored:

• Data pipeline activity – A data pipeline is a group of activities that together perform a data ingestion task. Pipelines allow you to manage, extract, transform, and load (ETL) activities together instead of individually. Monitor the success or failure of jobs and pipeline activities. Look for errors if the pipeline failed. View job history to compare current activity performance to past job execution performance to gain insight into when errors were first introduced into a process.
• Dataflows – A dataflow is a tool for ingesting, loading, and transforming data using a low-code interface. Dataflows can be run manually or scheduled or run as part of pipeline orchestration. Monitor start and end times, status, duration, and table load activities. To investigate issues, drill down into activities and view information about errors.
• Semantic model refreshes – A semantic model is a visual representation of a data model that’s ready for reporting and visualization. It contains transformations, calculations, and data relationships. Changes to the data model require the semantic model to be refreshed. Semantic models can be refreshed from data pipelines using the semantic model refresh activity. Monitor for refresh retries to help identify transient issues, before classifying an issue as a failure.
• Spark jobs, notebooks and lakehouses – Notebooks are an interface for developing Apache Spark jobs. Data can be loaded, or transformed for lakehouses using Spark and notebooks. Monitor Spark job progress, task execution, resource usage, and review Spark logs.
• Microsoft Fabric Eventstreams – Events are observations about the state of an object, like a timestamp for weather sensors. Eventstreams in Fabric are set up to run perpetually to ingest real-time or streaming events into Fabric and transform them for analytics needs, and then route them to various destinations. Monitor streaming event data, ingestion status, and ingestion performance.

Monitoring best practices

Continuously monitor the data ingestion, transformation, and load processes to ensure they’re running smoothly. Monitoring best practices include:

• Identifying what to monitor and tracking metrics.
• Collecting and analyzing data on a regular basis to identify normal behavior so you can spot anomalies when they occur.
• Reviewing logs and metrics regularly to identify and establish parameters for normal system behavior.
• Taking action to resolve problems when metrics and logs show deviations from normal behavior.
• Optimizing performance by using monitoring data to identify bottlenecks or performance issues.

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When educators write prompts for information, generative AI models don’t “know” an answer. Instead, it predicts the most likely response based on its training data. Regardless of the quality of your prompt, the model might generate an incorrect or fabricated response.

Misinformation can be spread through these fabrications. Copilot Chat aims to base all its responses on reliable sources—but AI-generated responses might be incorrect, and non-Microsoft content on the internet might not always be accurate or reliable. Copilot Chat might sometimes misrepresent the information it finds, and you might see responses that sound convincing but are incomplete, inaccurate, or inappropriate.

While Copilot Chat works to avoid sharing unexpected offensive content in search results and takes steps to prevent its chat features from engaging on potentially harmful topics, educators might still get unexpected results. Provide feedback or report concerns directly to Microsoft by using the feedback features beneath the response.

When Copilot Chat provides a response to a prompt, it also provides two key pieces of information: the search terms used to generate the response and the links to content sources. Educators can use these details to inform their evaluation of the response. If the prompt terms don’t represent the intended question, start a new prompt with different wording. If the source links aren’t reliable, ask Copilot Chat to refine the response using specific, more reliable websites that you provide.

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Based on Bing data, images are one of the most searched categories—second only to general web searches. Historically, search was limited to images that already existed on the web. Microsoft Designer and Visual Search help educators incorporate visual tools into their search and build understanding of new concepts.

There are two ways to work with images in Copilot Chat. The first way is to ask Copilot Chat to create a new image. The second way is to use Visual Search, which includes adding an image to the prompt.

Image creation by Microsoft Designer is powered by an advanced version of the DALL·E model from our partners at OpenAI. It creates an image simply by using words to describe the desired picture in over 100 languages.

Ask Copilot Chat to create a brand-new image with a prompt that begins with “create an image of” or “draw an image of.” Then, finish the prompt with an exact description. Image creation works best with lots of description, so add details like adjectives, locations, and artistic styles to help guide the output. Also consider using point of view and lighting direction.

Educators can use Copilot Chat to make images for class presentations, newsletters, quizzes, avatars, assignments, and more.

 Tip

Try one of the following sample prompts in Copilot Chat or write one based on your needs or interests. Visit copilot.cloud.microsoft to begin, then add your prompt to a new topic.

Create an image of a blue panda bear wearing sunglasses on the beach in digital art format.

Draw a 3D typography letter B on a green background with shiny chrome texture in a minimalist style.

Create a cartoon image of an apartment decorated in primary colors with a television, a couch, and a plant in one corner.

Draw an image of a sunset over the Roman coliseum in a realistic style to use for my course syllabus header.

When prompting Copilot Chat to generate an image, Microsoft Designer is invoked to create a graphic that matches the prompt description. Educators can use these images in class newsletters, presentations, lessons, and more. Microsoft Designer uses the latest DALL·E 3 model from OpenAI, which delivers a huge leap forward with more beautiful creations and better renderings than DALL·E 2.

Model digital citizenship for learners by acknowledging the images were created with AI and include the prompt as a teachable moment. Microsoft doesn’t claim ownership of the images created by Microsoft Designer.

Each image created is original, so images created by the sample prompts might be different with each chat. Regenerate another set of images if the first set isn’t ideal for your purpose.

These are sample images generated by the previous prompts.

Use Visual Search

With Visual Search in Copilot Chat, educators can input images and ask questions about them. Ask questions about images that are difficult to describe. For example: learn about a landmark you haven’t seen, identify a plant or animal you don’t recognize, and more.

To use Visual Search in Copilot Chat:

1. Select the Add an image icon in the text box in Copilot Chat.
2. In the Microsoft 365 Copilot app for mobile you can upload an image file or take a photo to add an image.
3. Ask a question related to the image.

Copilot Chat first analyzes the photo to blur faces for privacy, then interprets the image, searches for information about the image, and even provides additional details like a map or link to learn more.

Sample prompt

1. Find a photo of an iconic location around the world or type of animal.
2. Upload the photo to Copilot Chat’s Visual Search and add a prompt like one of the following examples:Explain to me where this statue is located. Include a map to the destination.Identify this animal. Give additional information about the animal’s habitat, food sources, and lifespan. Organize this information into a list.

Sample response

Following is a sample response for the first prompt.

Copilot sample prompt 5-Identify statue

Additional protections for images

Microsoft’s development of AI is guided by its Responsible AI principles to help deploy AI systems responsibly. To curb the potential misuse of image creation tools like Microsoft Designer, Microsoft works together with DALL·E’s developer OpenAI to deliver an experience that encourages responsible use with additional protections. For example, there are controls in place that aim to limit the generation of harmful or unsafe images. When Copilot Chat detects a potentially harmful image that could be generated by a prompt, it blocks the prompt and warns the user. Microsoft also makes it clear that Microsoft Designer’s images are generated by AI.

When you upload an image, Copilot Chat uses facial blurring and other safety mechanisms before sending the image to the AI model for processing. Facial blurring protects the privacy of people in the image. The face blurring technology relies on context clues to determine where to blur and attempts to blur all faces.

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Microsoft 365 Copilot Chat includes the functionality to interact with Pages to take Copilot Chat-generated content and put it in a dynamic, persistent canvas where users can edit it, add to it, share it, and work on it with others in real time.

Adding a Copilot Chat response to a page is simple. Below the Copilot Chat response is an option to either add that response to a new page by choosing Edit in Pages or to add that response to an existing page by choosing Add to recent page.

When adding the Copilot Chat response to a page, it opens next to the chat and copies the response with its formatting, ready to edit.

Continue the conversation with Copilot Chat and opt to add each new response to either the same page or another page. Meanwhile, everything on the page is editable either alongside the Copilot Chat or in the Microsoft Loop app. Changes made to pages are saved automatically and indefinitely unless deleted.

When a page is shared with a team or class, everyone can work collaboratively on the contents in real time. Everyone can see and edit the copied Copilot Chat responses and any other content.

To return to a previously created page, either open the page in the Microsoft Loop app or, if the page was created recently, find it in the Copilot Chat interface by selecting the Pages icon alongside the New chat option by the profile picture.

Example use of Copilot Chat with Pages

You’re working together with colleagues to plan a trip to the local science museum between the months of March to May with a class of 30 students aged 15-16. You write a prompt in Copilot Chat to generate a risk assessment for the trip in tabular format so it can be easily edited in a Microsoft Loop page. The intention is to assign responsibility for various actions to different staff members attending the trip. After Copilot Chat generates a response, select Edit in Pages and share the page with relevant school staff to edit the page together.

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Educators can take full advantage of the power of Copilot Chat by crafting good prompts. When writing prompts, consider the following tips.

• Define clear objectives. Determine the main goal of the prompt and the role AI should take. Whether you’re creating a syllabus, drafting a quiz, or revising lesson content, have a clear vision of the end goal.
• Be specific. Copilot Chat experiences work best when you give detailed instructions. Specify the grade level, subject, topic, or any other relevant parameters. For example, “secondary math quiz on algebraic expressions” is clearer than “math quiz.”
• Structure the prompt. Break complex tasks into smaller parts. Instead of asking the AI to draft an entire lesson, request an outline, then delve into specific sections.
• Iterate and refine. The first response from AI might not always align perfectly with expectations. Don’t hesitate to rephrase the prompt, ask follow-up questions, or provide more context based on the initial output.
• Combine expertise. Use AI as a tool to enhance and streamline work, but remember to overlay its suggestions with your educational expertise. AI can suggest content, but the educator decides the best way to edit and present it to their audience.

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cators can access the full experience of Microsoft Copilot Chat with enterprise data protection in several ways:

• Go directly to copilot.cloud microsoft
• From Microsoft Edge browser, launch Copilot in Edge
• Download the Microsoft 365 Copilot app

Sign in with your Microsoft account for Work or School to get the full Copilot Chat experience. If you don’t sign in, conversations are limited and don’t include access to all features.

To begin, enter a question to start the conversation.

Practice with Copilot Chat

 Tip

Work with the sample prompts to experience Copilot Chat or write unique prompts with similar parameters to fit your needs.

 Important

We recommend using the Microsoft Edge browser during your practice so you can experience all the features explored in this module.

Explore Copilot Chat features

For this activity, experiment with the various features in Copilot Chat. Some of these features might be temporarily unavailable due to app updates. Refer to the frequently asked questions about Copilot Chat to learn more.

• Rate: Rate the quality of the response.
• Copy: Select the Copy button and copy the response to another location.
• Share: Select the Share button and choose a way to share the response with someone else.
• Read aloud: Listen to the response read aloud through your device’s speakers.
• Chat history: Open the chat history to view previous chats from the same account.

Copilot Chat can be used for more than research. Educators can also use Copilot Chat in Edge to access chat features.

Practice with samples

 Tip

Work through the following sample prompts to experience Copilot Chat in Edge or use other websites or PDFs with similar parameters to fit your needs. If you experience any issues with this feature, you might need to change your Edge browser settings. You can customize your permissions in Microsoft Edge Settings > Sidebar > Copilot Chat.

Chat with Copilot Chat in the Edge sidebar

1. Open the Computational thinking PDF in Microsoft Edge and then open Copilot Chat by choosing the Copilot Chat icon in the Edge toolbar. Copilot Chat opens as an app in the Edge sidebar.
2. If the option is available, select Generate page summary. If the option isn’t available, enter:Summarize the main points of this page.
3. Once Copilot Chat creates the summary, follow up with another question.Thanks! Can you create a 5-question quiz related to this document?

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Artificial intelligence (AI) is already a part of our lives in many ways. We often rely on it for many things, including:

• Personalized recommendations on recipes, entertainment, news, purchases, and more
• Tailored assistance on spelling, grammar, or writing mechanics
• Targeted financial advice from online banking institutions
• Individualized health plans
• Predictive analysis and real-time routing recommendations

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Microsoft integrates AI into its operations with a strong emphasis on ethics and responsibility. The guiding principles of this approach are:

• Fairness. Microsoft aims to eliminate biases, ensuring equal treatment for all users.
• Reliability & Safety. Rigorous testing ensures Microsoft’s AI performs consistently and safely.
• Privacy & Security. Your data is protected. Both in training and post-deployment phases, Microsoft places a premium on safeguarding user details.
• Inclusiveness. AI tools are crafted to be accessible and beneficial for everyone, regardless of physical ability, gender, or ethnicity.
• Transparency. Microsoft believes in keeping users informed about how its AI systems work and their intended purposes.
• Accountability. Ethical and legal standards are at the forefront, with AI developers and designers held accountable for their creations.

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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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At its core, Microsoft 365 Copilot isn’t just another feature—it’s an intelligent partner that accompanies you throughout your day-to-day Microsoft 365 interactions. Be it in Outlook, PowerPoint, Word, Excel, Teams, or other applications, Copilot’s goal is to save you time by generating new content, offering relevant suggestions, and making you more productive.

Copilot’s understanding context and user needs

Microsoft 365 Copilot’s effectiveness stems from its unparalleled ability to understand you — the user. It does so by:

• Analyzing content. Whether it’s the document you’re drafting, the email you’re composing, or the meeting you’re in, Copilot scrutinizes the subject matter, tone, structure, and semantics to determine your intent and meaning.
• Getting context from your work data in Microsoft 365. Your communications, activity history, and content help Copilot to get additional context in real-time as it responds to your prompts.

Transform how you work

With a deep understanding of your context, Microsoft 365 Copilot doesn’t stop at just observations. It takes action:

• Search and retrieval. Copilot uses powerful search capabilities that identify useful data and content sources that can assist you.
• Natural phrasing with large language models. Large language models (LLMs) provide the engine that powers Copilot. These models enable Copilot to craft naturally phrased recommendations, ensuring that any content it generates aligns with your unique situation.
• Refining recommendations. It’s not about quantity, but quality. Copilot evaluates potential suggestions, refining them to ensure what you get is contextually relevant and specific.

Microsoft 365 Copilot is designed to transform how employees work in the digital age. The following list describes some of the many features that Copilot provides organizations, including:

• Enhanced meeting engagement. Copilot can help you stay more engaged in meetings. It also provides quick catch-ups for meetings you missed, ensuring you’re always in the loop.
• Efficient email management. Copilot can help streamline your email communication by summarizing lengthy email threads and drafting responses.
• Writing assistance. Copilot can transform your writing by drafting, editing, summarizing, and creating alongside you. This functionality can enhance the quality and efficiency of your documents.
• Presentation development. Starting a new presentation is simplified with Copilot. You can begin with either a prompt or an outline using natural language commands, bringing your ideas to life.
• Data analysis and visualization. Copilot can help identify trends, create visualizations, and provide recommendations, thereby simplifying data analysis.
• Security and compliance. Your data remains protected with comprehensive enterprise compliance and security controls, ensuring peace of mind.
• User control. You maintain control over AI suggestions, deciding which to use, modify, or discard. This design ensures the human element remains at the forefront of AI interaction, with the latest enhancements providing even greater flexibility and control.

Logical architecture

Microsoft 365 Copilot uses your organization’s data that you as an individual user have access to. For example, calendar events, emails, chats, documents, and meetings from the Microsoft Graph. It maps this data and relationships, providing personalized, relevant, and actionable information. Your data remains secure within the Microsoft 365 service boundary, adhering to the latest security, compliance, and privacy policies. Furthermore, communication between your tenant and Copilot components is encrypted.

 Note

The way Microsoft 365 Copilot accesses data is illustrated in the following diagram.

Here’s an explanation of how Microsoft 365 Copilot works:

1. Copilot receives an input prompt from a user in an app, such as Word or PowerPoint.
2. Copilot then preprocesses the input prompt through an approach called grounding. Grounding improves the specificity of the prompt, to help you get answers that are relevant and actionable to your specific task. The prompt can include text from input files or other content discovered by Copilot, and Copilot sends this prompt to the LLM for processing. Copilot only accesses data that an individual user has existing access to, based on, for example, existing Microsoft 365 role-based access controls.
3. Copilot takes the response from the LLM and post-processes it. This post-processing includes other grounding calls to Microsoft Graph, responsible AI checks, security, compliance and privacy reviews, and command generation.
4. Copilot returns the response to the app, where the user can review and assess the response.

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