Write effective Copilot prompts for Microsoft Power Platform

  • 8 minutes

If you ever wished for an intelligent assistant to streamline workflows, analyze data, or build applications, Copilot is designed to turn that aspiration into reality. However, there's an important caveat: Copilot’s effectiveness is directly tied to the quality of the prompts it receives. Think of it as providing directions to a friend—if your instructions are vague, they're likely to lose their way. Therefore, crafting clear, well-structured prompts is the key to unlocking Copilot’s full capabilities.

Best practices for effective prompts

To summarize, here are some best practices for writing effective prompts that maximize Copilot’s capabilities:

  • Be specific: Provide clear, detailed instructions to eliminate ambiguity.

  • Use natural language: Write prompts conversationally, as if speaking to a colleague.

  • Provide context: Include relevant background information to help Copilot understand your intent.

  • Iterate and refine: Treat the process as collaborative, adjusting prompts as needed to achieve the desired outcome.

  • Validate outputs: Review Copilot’s responses carefully and make corrections if necessary.

Let's discuss there in more detail.

The importance of precision in prompts

Imagine you're working in Power Apps and want to modify the appearance of a button. Instead of issuing a general command such as, "Make the button blue", consider being more precise: "Change the text color of Button_1_2 to blue". This level of specificity provides Copilot with exactly the information it needs, eliminating guesswork. It's akin to handing over a detailed map rather than simply pointing in a general direction.

Clarity and specificity are essential when writing prompts. A well-crafted prompt should include enough detail to guide Copilot effectively while avoiding ambiguity. For example, instead of saying, “Create a report,” you might say, "Generate a report summarizing monthly sales data from the ‘Orders’ table, grouped by region." This ensures Copilot understands your intent and delivers results that align with your expectations.

Conversational and natural language interaction

Maintain a conversational tone. Copilot is optimized for natural language, so interacting with it as you would with a colleague can enhance its understanding. For example, if you're building a workflow in Power Automate, you might say, “Create a flow that sends an email notification whenever a new order is added to the Dataverse table.” By being both clear and approachable, you help Copilot interpret your intent with greater accuracy.

Using natural language also allows you to refine your prompts iteratively. If the initial output does not meet your needs, you can adjust your phrasing or add more context to guide Copilot toward the desired outcome. Think of this process as a dialogue where you and Copilot collaborate to achieve the best possible result.

Providing context for better results

Providing context is critical. Suppose you are using Power BI to design a dashboard. Rather than requesting Copilot to "add a chart", offer the necessary details: "Add a bar chart to visualize monthly sales data from the ‘Orders’ table." The more context you supply, the better equipped Copilot is to deliver results that align with your expectations.

Context can include information about the data source, the specific task you are trying to accomplish, or the format of the desired output. For example, if you need Copilot to create a formula in Power Apps, you might say, "Write a formula for Button_1_2 that navigates to the ‘HomeScreen’ when clicked." Including relevant details reduces ambiguity and ensures Copilot understands the scope of your request.

Iteration and validation: A collaborative process

Always review Copilot’s output. While it's highly capable, it's not infallible. If something appears incorrect, you can refine your prompt or undo any changes. Think of this as a collaborative process where you and Copilot work together to achieve the desired outcome. (The Undo option is useful for rolling back edits—simply click it, and you're all set.)

Iteration is a natural part of working with AI tools like Copilot. If the initial response doesn't fully meet your needs, consider rephrasing your prompt, adding more context, or breaking down complex tasks into smaller, manageable steps. For example, instead of asking Copilot to "build an app", you might start with "Create a form to collect customer feedback, including fields for name, email, and comments".

By following these strategies, you can harness the full power of Copilot to enhance your experience across Power Platform. Whether you're building apps, automating workflows, or analyzing data, effective prompting is the foundation for achieving exceptional results.

Creating an app to manage employee data: A prompt journey

An HR team needed a Power Apps solution to manage employee data, including roles, departments, and onboarding progress. To streamline development, they turned to Copilot in Power Apps. However, they quickly realized that the quality of their prompts directly influenced the app's effectiveness. By refining their prompts and applying best practices, the HR team was able to achieve the desired outcome. Next, let's look at a progression of prompts, from basic to detailed, and how each impacts the final app.

Prompt 1: Basic request
"Create an app to manage employee data."

With this simple prompt, Copilot generated a basic app featuring a single Dataverse table labeled "Employee Data." The table included generic fields such as "Name," "Department," and "Start Date." While functional, the app lacked essential features like validation rules, structured relationships between data tables, and the ability to track onboarding progress. Significant manual adjustments would be required to make the app scalable and aligned with the HR team’s needs. This outcome highlighted the limitations of vague prompts.

Prompt 2: More detailed request
"Create an app to manage employee data, including roles and departments. Add validation rules for required fields."

This refined prompt resulted in a more structured app. Copilot created multiple Dataverse tables, such as "Employees," "Departments," and "Roles," with defined relationships between them. Validation rules were applied to ensure that critical fields like "Name" and "Start Date" were completed before submission.

Prompt 3: Detailed and contextual request
"Develop an app to manage employee data, including roles, departments, and onboarding progress. Ensure the app includes validation rules for required fields, establishes relationships between tables for roles and departments, and features a dashboard to monitor onboarding status in real time."

With this detailed and specific prompt, Copilot delivered a robust and comprehensive app. It generated interconnected Dataverse tables for "Employees," "Departments," and "Roles," ensuring seamless relationships between the data. Validation rules were implemented to maintain data integrity, and a dynamic dashboard was included to provide real-time insights into onboarding progress. The app was highly scalable, user-friendly, and required minimal extra customization. This version perfectly aligned with the HR team’s needs, demonstrating the value of crafting precise and detailed prompts.

So, whether you’re designing apps, automating workflows, analyzing data, or building customer portals, the key to success is crafting prompts that are clear, specific, and packed with context. With a little practice, you’ll be speaking Copilot’s language like a pro—and watching it turn your ideas into reality faster than you ever thought possible.