microsoftcopilot
4 TopicsTuesday Prompt Day | 6W + E Practical Experiment — From Information to Action
A useful AI response is not always an actionable response. In enterprise environments, Copilot can summarize information, identify patterns and suggest recommendations. But before acting on those recommendations, we need to ask: What is the evidence behind the recommendation? This is where the E in 6W + E becomes important. The scenario Imagine you provide Copilot with customer issues collected from different sources and ask: "Review these customer issues and suggest what we should do." The response may look useful. But is it enough to make a business decision? Let's improve the prompt using 6W + E. The improved prompt Act as an enterprise service improvement advisor. Review the customer issues provided in the source material. Identify the issues that have the greatest impact on customer experience. Analyze recurring issues, business impact, frequency, severity and any existing mitigation mentioned in the source material. The audience is senior service and business leadership. Use only the information available in the source material. Structure the response using: Key issue Evidence Business impact Recommended action Expected outcome Information still required Prioritize recommendations based on business impact and urgency. Evaluate the evidence behind each recommendation and clearly distinguish between confirmed facts, observations and assumptions. What changes? Instead of simply asking Copilot for recommendations, we have now given it: A clear role A defined problem A specific audience Source boundaries A decision-oriented structure Prioritization criteria An evaluation requirement For example, Copilot might identify recurring service delays as an important issue. But the next question should be: What evidence supports this recommendation? The EVALUATE step Before accepting the recommendation, evaluate: Is the recommendation supported by the available evidence? Is the suggested root cause actually confirmed? Is the business impact supported by the source material? Are facts being separated from assumptions? Is the recommended action realistic? Why has this issue been prioritized? What information is still missing? This changes the interaction from: Prompt → Answer to: Prompt → Output → Evaluate → Refine → Better Output The REFINE step If Copilot makes an unsupported assumption, refine the prompt. For example: "Do not infer a root cause unless it is supported by the source material. Clearly distinguish between confirmed evidence, reasonable observations and unverified hypotheses. For every recommendation, explain the evidence supporting it. If important information is missing, identify exactly what information is required before making the decision." Now the objective is not simply to get another answer. The objective is to improve the quality of the decision. My takeaway A response can look plausible and still be risky. The real value of Copilot comes from creating a repeatable process for questioning, evaluating and refining the output. That is why I see EVALUATE as more than the final step of a prompt. It can become the feedback loop that continuously improves the interaction. How do you use Copilot recommendations in your work? Do you accept the first useful-looking answer, or do you evaluate the evidence and refine the prompt before taking action? Share your approach in the comments. Please see the Resources section for the previous 6W + E experiments in this series.18Views0likes0CommentsTuesday Prompt Day | 6W + E Practical Experiment #4: From Information to Prioritization
Last week, we explored an important idea: A good prompt doesn't just ask AI for an answer. It gives AI enough context to produce an answer that can actually be evaluated and improved. But there is another challenge. What happens when Copilot has the right information… …but gives the wrong priority? Imagine asking Copilot to review a project update containing: An unresolved business dependency A resource constraint A minor documentation delay All three may be correct. But are they equally important? No. This is where prompt engineering moves beyond information retrieval. The goal is not just: "Give me the information." The goal becomes: "Help me identify what matters most." Using the 6W + E approach, we can make that expectation explicit: WHO → Who is making the decision? WHAT → What information needs to be considered? WHY → Why does the decision matter? WHEN → When is action required? WHERE → Where is the impact? WHICH → Which items should receive priority? E → What evidence supports the prioritization? Now the output becomes more useful: 🔴 High Priority Unresolved business dependency → Directly affects the project outcome. 🟡 Medium Priority Resource constraint → May affect execution if not addressed. 🟢 Lower Priority Documentation delay → Important, but limited immediate business impact. Notice what changed. The information didn't change. The priority did. And that leads to an important prompt-engineering principle: Correct information ≠ Useful answer A useful AI response should be: Relevant → Prioritized → Evidence-based → Actionable This is the next step in our 6W + E journey. We are moving from: Prompt → Output to: Prompt → Output → Evaluate → Prioritize → Refine → Act That's where prompt engineering starts becoming a practical decision-making discipline rather than simply a way to get better AI responses. 💡 My takeaway: Don't only ask AI: "What is the information?" Also ask: "What matters most, why does it matter, and what evidence supports that priority?" Next Tuesday, we'll take this one step further.37Views0likes0CommentsIs Your AI Prompt Missing the Real Problem? | Introducing the 6W + E Framework
One thing I’ve noticed while working with Generative AI and Microsoft Copilot: Sometimes the problem isn't the AI. It's the way we think before we prompt. We often write: “Create a presentation on AI.” “Summarize this document.” “Write an email to the customer.” The AI can certainly do these tasks. But will the output be what we actually need? I've been working on a simple principle to make prompting easier to remember: 6W + E WHY → WHAT → WHO → WITH → WAY → WIN → EVALUATE Here’s how I think about it: WHY — Why are we asking AI to do this? WHAT — What exactly do we want? WHO — Who is the audience or stakeholder? WITH — What context, data, documents or tools should AI work with? WAY — How should the output or task be delivered? WIN — What does a successful outcome look like? EVALUATE — Did the result actually achieve what we wanted? The last one is particularly important. Good prompting shouldn't be: Prompt → Answer → Done It should be: Think → Prompt → Evaluate → Refine I don't see 6W + E as a formula for writing longer prompts. I see it as a way to think more clearly before asking AI to work. And as we move from prompting to Copilot, AI workflows and AI agents, I believe this way of thinking becomes even more important. I'm going to explore this with practical Copilot examples in our upcoming Tuesday Prompt Day discussions. But before we get there, I'd like to start with the community: 👉 Which of these do you most often forget when prompting AI? WHY | WHAT | WHO | WITH | WAY | WIN | EVALUATE And do you normally evaluate and refine the first response—or accept it as it is? I'm curious to hear how others approach this.Solved346Views0likes5Comments🚀 Prompt Tuesday | Write Prompts Like a Pro
🚀 Prompt Tuesday | PT-001 | Create Professional Meeting Minutes with Microsoft Copilot Have you ever asked AI: "Summarize this meeting." The result is often too generic. Instead, assign the AI a role and define exactly what you need. ❌ Basic Prompt Summarize this meeting. ✅ Better Prompt You are an Executive Assistant responsible for documenting meetings. Review the meeting transcript below and generate professional meeting minutes. Include: • Meeting objective • Key discussion points • Decisions made • Action items • Owner for each action item • Due dates (if mentioned) • Risks or blockers • Open questions • Executive summary (5 bullet points) Format the output using clear headings and tables where appropriate. Meeting Transcript: <Paste transcript here> 💡 Why This Prompt Works This prompt gives the AI: Role → Executive Assistant Task → Generate structured meeting minutes Output Format → Headings and tables Expected Sections → Decisions, actions, risks, and summaries The result is a document that's ready to share with your team, with minimal editing. 🤖 Microsoft Copilot Tip If you're using Microsoft 365 Copilot in Teams or Word, don't stop at "Summarize this meeting." Try prompts like: Summarize this meeting for senior leadership. Highlight strategic decisions, unresolved issues, assigned action items, and any risks that require executive attention. Present the output in a concise table followed by a one-paragraph executive summary. Adding the intended audience helps Copilot tailor the response appropriately. 💬 Discussion Question What's the one prompt you use most often with Microsoft Copilot or another AI assistant? Share it in the comments—you might inspire someone else's next productivity221Views3likes0Comments