Category: Uncategorized

To get the best out of AI, you want to create prompts that help it give you useful responses. A prompt is simply a question or instruction you submit to get a specific response. It’s like asking a friend or coworker for help or information, but instead you’re asking an AI, like Microsoft Copilot. The clearer and more detailed your prompt, the better Copilot can understand and respond. It’s often more effective to tell Copilot what you want it to do, rather than what you don’t want it to do.

Write effective prompts

An effective prompt should be clear, specific, contextual, and goal oriented. By incorporating these elements into your prompts, you can ensure that you get more accurate and relevant responses from AI tools. Remember, the more information and guidance you provide in your prompt, the better Copilot can assist you.

A prompt can be simple or detailed, but you must have a clear goal. If you want to be more specific, add additional information. Often, you need more than just a goal to achieve the results you want. The following video describes the four elements of a good prompt: Goal, Context, Sources, and Expectations.

Here are some tips to keep in mind when writing a prompt:

• Be specific about what you want Copilot to do. Clear goals lead to better responses.
• Add some context to help Copilot understand what you’re asking. Context makes the response more relevant.
• Provide some data or information for Copilot to use. This helps ground the response in the right context.
• Let Copilot know how you want the response to be formatted. This sets clear expectations.

Explore good, better, and best prompts

The following table includes some everyday tasks that you could co-create with AI, and include examples of good, better, and best prompts. These examples help you understand how to create prompts to make your tasks more simple and effective.

 Tip

When you see text in brackets within a prompt, it indicates where you should either paste your own content or replace the bracketed text with your own ideas. After personalizing the prompt, remove the brackets. For example, you might replace [my workout] with morning run and [upbeat and energetic] with relaxing and calm to make this prompt your own.

The following image shows the playlist Copilot generated using the “best” prompt example. Note that it’s formatted as a table and includes all of the requested details for each song.

 Note

One of the best ways to effectively work with Copilot is to ask it to refine the results. When you give a prompt to an AI, the first response might not be exactly what you want. So, look at the result, think about what you want to change, and then try again with a slightly different prompt. Each time you make a change, you’re getting closer to the perfect result. This is called iterating.

For example, let’s say you used Copilot to create a menu for a dinner party, but you forgot you need to include vegetarian options. All you need to do is ask Copilot to update the menu to include some vegetarian dishes. This way, you can keep refining your prompts until you get the perfect result.

Find inspiration

The following table includes some prompt resources you might find helpful to get started with more complex prompting.

https://cosmicnext.com/about-2

AI can simplify everyday tasks and enhance productivity. This unit introduces the basics of AI, including how generative AI creates new content, and how tools like Microsoft Copilot can enhance productivity. You also explore the importance of using AI responsibly to ensure fairness, transparency, and trust.

What is AI, and how does it work?

Generative AI focuses on creating new, unique content, based on the input you provide. This input is called prompting, which just means asking AI for specific things. Generative AI can even produce creative content, such as writing poems, composing melodies, or designing graphics, based on the patterns and styles it learned from existing data.

People usually interact with generative AI built into a chat application. One example of such an application is Microsoft Copilot, an AI-powered productivity tool designed to enhance your work experience by providing real-time intelligence and assistance. In other words, it’s a smart tool that helps you work better by giving you quick answers and help when you need it.

 Note

Behind the scenes

Generative AI uses large language models (LLMs), which are AI systems trained on massive amounts of text, to understand and generate human-like language. They are like a very intelligent autocomplete. LLMs are a type of AI model that uses natural language processing (NLP) techniques to work.

NLP is a broader technology that helps computers understand and use human language to make things like chatbots, voice assistants, and translations work smoothly. LLMs are one powerful example of NLP in action.

Help with everyday tasks

Copilot is a smart assistant that can simplify your daily routines by helping you with various tasks and make your life a bit easier and more organized. Whether you’re looking to get creative, plan your day, or need some personal assistance, AI can make your life more manageable. Let’s look at some everyday examples of how you might use AI.

Write creative content

Struggling to start a project or come up with new ideas? Whether you’re drafting an email, writing a blog post, or brainstorming for a podcast, AI can give you a starting point. It offers suggestions, generates text, and can even create entire pieces of content. For example, Copilot can help you:

• Compose emails. Quickly draft professional or personal emails by inputting the main message and letting AI create a draft.
• Generate blog posts. Create engaging blog posts by providing a topic and key points, and AI generates a well-structured article.
• Brainstorm ideas. Get creative ideas for your next project, whether it’s a slogan, a story, or a social media post.

By using AI for creative writing and content creation, you can enhance your productivity and ensure that your materials are engaging and effective.

Organize and plan

AI can help you stay organized and plan your activities by providing personalized recommendations and streamlining your planning process. For example, Copilot can help you:

• Plan a trip. AI can help you organize your travel itinerary, suggest destinations, flights, and accommodations, and even recommend activities based on your preferences.
• Manage and organize your calendar. AI can help you schedule appointments, set reminders, and organize your daily tasks to ensure you stay on track.
• Plan an event. Whether you’re organizing a business conference or a family gathering, AI can help you plan the event by suggesting venues, creating schedules, and managing guest lists. It can also provide reminders and updates to keep everything on track.
• Manage tasks. AI can help you manage your to-do list by prioritizing tasks, setting deadlines, and providing reminders. It can also suggest ways to break down larger projects into manageable steps.

By using AI for organizing and planning, you can save time, reduce stress, and ensure that everything runs smoothly.

Get personal assistance

Copilot can act as your personal assistant and help you manage your life. Whether you need reminders, information, or assistance with tasks, AI is there to support you. For example, Copilot can help you:

• Answer questions. Get quick answers to your questions, whether it’s finding information online, checking the weather, or getting directions.
• Provide recommendations. Receive personalized recommendations for movies, books, restaurants, and more based on your preferences and past choices.
• Automate tasks. Automate routine tasks such as setting up meetings, sending follow-up emails, or managing your to-do list.

With AI as your personal assistant, you can streamline your daily activities, stay organized, and focus on what matters most to you.

Responsible AI

AI should treat everyone fairly, protect privacy, and operate transparently. This helps build trust and ensures that AI systems are used in ways that respect individuals’ rights and interests. In this short video, learn about some of the challenges and solutions related to AI accuracy.

As AI becomes more integrated into our daily lives, it’s important to use it responsibly. Here are some ways you can help keep your AI-generated content fair, trustworthy, and beneficial to society:

• Provide clear and specific prompts. Avoid sensitive topics and always review and validate outputs for accuracy and relevance.
• Watch for common biases:
  • Gender bias. AI systems can perpetuate gender stereotypes if trained on biased data. For example, a hiring algorithm might favor male candidates over female candidates due to historical data biases.
  • Racial bias. AI systems can exhibit racial bias, leading to discriminatory outcomes. For instance, facial recognition systems might have higher error rates for people of color compared to white individuals.
  • Socioeconomic bias. AI systems can favor individuals from higher socioeconomic backgrounds. A credit scoring algorithm, for example, might disproportionately favor applicants from wealthier neighborhoods.

Now that you have an idea of what generative AI is, how it can enhance our productivity and creativity, and the ethical considerations of using it, let’s explore how to interact with AI through prompting.

https://cosmicnext.com/about

Raw streaming data rarely arrives in the exact format needed for analysis or action. Transformations allow you to clean, enrich, and reshape data before routing it to destinations, ensuring each endpoint receives data optimized for its specific purpose.

Common transformation scenarios include:

• Data quality: Filter out invalid or incomplete data before processing
• Content-based routing: Route different data subsets to appropriate destinations based on the actual data values or content
• Data enrichment: Add calculated fields, rename columns for clarity, or convert data types for downstream compatibility
• Aggregation and summarization: Calculate running totals, averages, or counts over time windows for dashboard displays
• Format standardization: Ensure consistent data structure across multiple data sources before combining streams

Transform event data

The eventstream canvas gives you a way to create event data processing workflows. Eventstream provide several no-code transformations that you can drag onto the canvas:

• Filter: Filter events based on the value of a field in the input. Keep only events that meet specific conditions. For example: temperature > 80°, status = “error”, customer type = “premium”.
• Manage fields: This transformation allows you to add, remove, change data type, or rename fields coming in from an input or another transformation. Add calculated fields, remove unnecessary columns, rename fields, or change data types to match destination requirements.
• Aggregate: Use the aggregate transformation to calculate an aggregation (Sum, Minimum, Maximum, or Average) every time a new event occurs over a period of time. This operation also lets you rename calculated columns, and filter the aggregation based on other dimensions in your data. You can have one or more aggregations in the same transformation.
• Group by: Calculate aggregations across events within time windows, for example, hourly sales totals, or daily temperature averages. This transformation supports various time windows including tumbling windows (fixed intervals) and sliding windows (overlapping intervals).
• Union: Use the union transformation to connect two or more nodes in the event canvas and add events with shared fields (with the same name and data type) into one table. Fields that don’t match are dropped and not included in the output.
• Join: Combine data from two streams based on a matching condition between them.
• Expand: Use this array transformation to create a new row for each value within an array.

Create transformation workflows

Transformations can be used together to create data processing pipelines. For example, if you had a stream of equipment temperature readings, you could start by using filter to remove sensor errors from incoming IoT data. Next, you might use manage fields to add a calculated “priority” column based on temperature thresholds. Then group by could calculate hourly averages by location. Finally, you’d route the processed data to appropriate destinations: temperature data to Fabric Activator for rule evaluation and hourly summaries to a Lakehouse for historical analysis.

https://cosmicnext.com/design

Once you create an eventstream in Fabric, you can connect it to a wide range of data sources, optionally transform it, and route the transformed, or processed data to multiple destinations. In this unit, we’ll review eventstream sources and destinations.

Eventstream sources

You can stream data from Microsoft sources and also ingest data from non-Microsoft platforms including:

• Microsoft sources, like Azure Event Hubs, Azure IoT Hubs, Azure Service Bus, Change Data Capture (CDC) feeds in database services, and others.
• Azure events, like Azure Blob Storage events.
• Fabric events, such as changes to items in a Fabric workspace, data changes in OneLake data stores, and events associated with Fabric jobs.
• External sources, such as Apache Kafka, Google Cloud Pub/Sub, and MQTT (Message Queuing Telemetry Transport)

Configure eventstream sources

After you create an eventstream, you can add data sources using the eventstream canvas. You can either create a new source or connect to an existing source from the Real-Time Hub:

 Tip

To see all supported sources, see Add and manage eventstream sources.

Eventstream destinations

Streaming data requires immediate processing and storage to retain its value. Destinations in an eventstream serve as endpoints where your processed data becomes available for queries, reports, dashboards, alerts, actions, or integration with other systems. You can load the data from your stream into the following destinations:

• Eventhouse: This destination lets you ingest your real-time event data into an Eventhouse, where you can use Kusto Query Language (KQL) to query and analyze the data.
• Lakehouse: This destination gives you the ability to transform your real-time events before ingesting them into your lakehouse. Real-time events are converted into Delta Lake format and then stored in designated lakehouse tables.
• Derived stream: You can think of derived streams as transformed versions of your original data stream that enable content-based routing. Derived streams let you route subsets of data from your default or original stream to different destinations based on the content of data. For example, you could filter IoT sensor data to send high-temperature alerts to Fabric Activator while routing hourly averages to a KQL database.
• Fabric Activator: Directly connect your real-time event data to an event detection engine that automatically triggers actions when specific patterns or conditions are detected in your streaming data. When data reaches certain thresholds or matches patterns, Activator can send notifications, launch Power Automate workflows, or trigger other automated responses.
• Custom endpoint: With this destination, you can route your real-time events to a custom endpoint. This destination is useful when you want to direct real-time data to an external system or custom application outside Microsoft Fabric.

You can attach to multiple destinations within an event stream at the same time without impacting or colliding with each other.

 Tip

For more information about supported destinations, see Add and manage a destination in an eventstream.

Configure eventstream destinations

Eventstream destinations can be configured in the eventstream canvas. A destination can be specified after a datasource is connected or after optional transformations are applied.

The eventstream canvas in the image shows:

• Add destination dropdown: for configuring new destinations
• Three configured destinations: a derived stream, a Fabric Activator, and an Eventhouse
• Content-based routing where the output of the GroupByStreet transformation is routed to a derived stream that’s then routed to both an Activator to check if there are bikes at every station and to an Eventhouse to insert bike counts by street into a KQL database

https://cosmicnext.com/sample-page

The Eventstream feature in Fabric works by creating a pipeline that ingests events from streaming data sources, processes them through optional transformations, and delivers them to various destinations. Eventstream is the delivery mechanism that carries events from where they happen to where they need to be processed, analyzed, or acted upon.

You can use the eventstream canvas, which is a visual editor, to design your pipeline by dragging and dropping different nodes, such as sources, transformations, and destinations. You can also see the event data flowing through the pipeline in real-time. You don’t need to write any code or manage any infrastructure to use Eventstream.

This image shows the eventstream canvas. There’s a real-time data source called Bicycles, which includes: city bike rental data including bike locations, bike station street names and more. Bicycle-data is an eventstream that ingests data from the Bicycles data source. The data is transformed by an operation named GroupByStreet that sums the number of bikes by bike station street name. This data is stored in a table in an Eventhouse called Bikes-by-street-table.

The main components of an eventstream are:

• Sources: Sources are where your event data comes from. You can stream data from Microsoft sources and also ingest data from non-Microsoft platforms.
• Transformations: You can transform the data as it flows in an eventstream, enabling you to filter, summarize, and reshape it before storing it. Examples of available transformations include: SQL code, filter, manage fields, aggregate, group by, expand and join.
• Destinations: Destinations are where your transformed event data goes for storage, further processing, alerts, or integration with other systems. You can route the data from your stream to various destinations such as tables in an Eventhouse or lakehouse, custom endpoints, derived streams for more processing, or Fabric Activator to trigger actions.

https://lernix.com.my/vmware-vsphere-install-configure-manage-v8-icm-malaysia

Key points to understand about responsible AI include:

• Fairness: AI models are trained using data, which is generally sourced and selected by humans. There’s substantial risk that the data selection criteria, or the data itself reflects unconscious bias that may cause a model to produce discriminatory outputs. AI developers need to take care to minimize bias in training data and test AI systems for fairness.
• Reliability and safety: AI is based on probabilistic models, it is not infallible. AI-powered applications need to take this into account and mitigate risks accordingly.
• Privacy and security: Models are trained using data, which may include personal information. AI developers have a responsibility to ensure that the training data is kept secure, and that the trained models themselves can’t be used to reveal private personal or organizational details.
• Inclusiveness: The potential of AI to improve lives and drive success should be open to everyone. AI developers should strive to ensure that their solutions don’t exclude some users.
• Transparency: AI can sometimes seem like “magic”, but it’s important to make users aware of how the system works and any potential limitations it may have.
• Accountability: Ultimately, the people and organizations that develop and distribute AI solutions are accountable for their actions. It’s important for organizations developing AI models and applications to define and apply a framework of governance to help ensure that they apply responsible AI principles to their work.

Responsible AI examples

Some example of scenarios where responsible AI practices should be applied include:

• An AI-powered college admissions system should be tested to ensure it evaluates all applications fairly, taking into account relevant academic criteria but avoiding unfounded discrimination based on irrelevant demographic factors.
• An AI-powered robotic solution that uses computer vision to detect objects should avoid unintentional harm or damage. One way to accomplish this goal is to use probability values to determine “confidence” in object identification before interacting with physical objects, and avoid any action if the confidence level is below a specific threshold.
• A facial identification system used in an airport or other secure area should delete personal images that are used for temporary access as soon as they’re no longer required. Additionally, safeguards should prevent the images being made accessible to operators or users who have no need to view them.
• A web-based chatbot that offers speech-based interaction should also generate text captions to avoid making the system unusable for users with a hearing impairment.
• A bank that uses an AI-based loan-approval application should disclose the use of AI, and describe features of the data on which it was trained (without revealing confidential information).

https://lernix.com.my/ccna-certification-training-courses-malaysia

Key points to understand about generative AI include:

• Generative AI is a branch of AI that enables software applications to generate new content; often natural language dialogs, but also images, video, code, and other formats.
• The ability to generate content is based on a language model, which has been trained with huge volumes of data – often documents from the Internet or other public sources of information.
• Generative AI models encapsulate semantic relationships between language elements (that’s a fancy way of saying that the models “know” how words relate to one another), and that’s what enables them to generate a meaningful sequence of text.
• There are large language models (LLMs) and small language models (SLMs) – the difference is based on the volume of data and the number of variables in the model. LLMs are very powerful and generalize well, but can be more costly to train and use. SLMs tend to work well in scenarios that are more focused on specific topic areas, and usually cost less.

Generative AI scenarios

Common uses of generative AI include:

• Implementing chatbots and AI agents that assist human users.
• Creating new documents or other content (often as a starting point for further iterative development)
• Automated translation of text between languages.
• Summarizing or explaining complex documents.

https://lernix.com.my/clients

Key points to understand about using AI to extract data and insights include:

• The basis for most document analysis solutions is a computer vision technology called optical character recognition (OCR).
• While an OCR model can identify the location of text in an image, more advanced models can also interpret individual values in the document – and so extract specific fields.
• While most data extraction models have historically focused on extracting fields from text-based forms, more advanced models that can extract information from audio recording, images, and videos are becoming more readily available.

Data and insight extraction scenarios

Common uses of AI to extract data and insights include:

• Automated processing of forms and other documents in a business process – for example, processing an expense claim.
• Large-scale digitization of data from paper forms. For example, scanning and archiving census records.
• Indexing documents for search.
• Identifying key points and follow-up actions from meeting transcripts or recordings.

https://lernix.com.my/index

Key points to understand about natural language processing (NLP) include:

• NLP capabilities are based on models that are trained to do particular types of text analysis.
• While many natural language processing scenarios are handled by generative AI models today, there are many common text analytics use cases where simpler NLP language models can be more cost-effective.
• Common NLP tasks include:
  • Entity extraction – identifying mentions of entities like people, places, organizations in a document
  • Text classification – assigning document to a specific category.
  • Sentiment analysis – determining whether a body of text is positive, negative, or neutral and inferring opinions.
  • Language detection – identifying the language in which text is written.

 Note

In this module, we’ve used the term natural language processing (NLP) to describe AI capabilities that derive meaning from “ordinary” human language. You might also see this area of AI referred to as natural language understanding (NLU).

Natural language processing scenarios

Common uses of NLP technologies include:

• Analyzing document or transcripts of calls and meetings to determine key subjects and identify specific mentions of people, places, organizations, products, or other entities.
• Analyzing social media posts, product reviews, or articles to evaluate sentiment and opinion.
• Implementing chatbots that can answer frequently asked questions or orchestrate predictable conversational dialogs that don’t require the complexity of generative AI.

https://lernix.com.my/comptia-training-courses-malaysia

Key points to understand about speech include:

• Speech recognition is the ability of AI to “hear” and interpret speech. Usually this capability takes the form of speech-to-text (where the audio signal for the speech is transcribed into text).
• Speech synthesis is the ability of AI to vocalize words as spoken language. Usually this capability takes the form of text-to-speech in which information in text format is converted into an audible signal.
• AI speech technology is evolving rapidly to handle challenges like ignoring background noise, detecting interruptions, and generating increasingly expressive and human-like voices.

AI speech scenarios

Common uses of AI speech technologies include:

• Personal AI assistants in phones, computers, or household devices with which you interact by talking.
• Automated transcription of calls or meetings.
• Automating audio descriptions of video or text.
• Automated speech translation between languages.

https://lernix.com.my/exchange-server-certification-training-courses-malaysia