Want BETTER AI Results? Master Your Copilot Prompts Now

In the premiere episode of the Copilot Chronicles, Amelia Roberts walks us through how to craft perfect prompts to maximize the effectiveness of Microsoft Copilot, especially in the web setting via Microsoft Edge. Whether you're planning a trip or solving business tasks, the quality of your prompts significantly impacts the usefulness of the AI-generated responses. This episode is a foundational guide to prompt engineering designed for learners, professionals, and enthusiasts alike.

Key Ingredients of the Perfect Prompt

Amelia introduces a structured, four-part framework to build more effective prompts:

1. Goal
   Start with a clear and specific goal. This is your primary objective—what do you want the AI to help you achieve? Example: “I want to plan a family vacation to Disney World.”
2. Context
   Provide background information that helps Copilot understand the goal more precisely. Set the scene so the AI tailors the answer appropriately. Example: “We are a family of four with two kids aged 7 and 10, visiting in summer 2025.”
3. Expectations
   Define how you want the results to be delivered. Specify the format, tone, detail level, or any must-have items in the response. Example: “I’d like a detailed outline on attractions for young kids, family-friendly restaurants, and any special events or parades.”
4. Source
   While optional, identifying preferred sources improves accuracy. Suggesting credible platforms such as official websites or recent reviews increases the reliability of the AI's response. Example: “Use the official Disney World website and TripAdvisor reviews.”

Testing Prompts: Bad vs. Good

Amelia demonstrates a live example using Copilot in Microsoft Edge’s free web setting. First, she submits a vague prompt: “Tell me what to do at Disney World.”

The result is somewhat helpful, providing general activities sorted by parks (Magic Kingdom, Epcot, etc.) but lacks personalization. It fails to factor in her children’s ages or offer tailored food recommendations—essentially missing key context and expectations.

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