Tuesday Prompt Day | 6W + E Practical Experiment: Don't Just Accept the First AI Answer
Last week, we used 6W + E to move from: AI-generated information → Actionable recommendations But there is another important question: What if the recommendation itself is based on an assumption? This is where prompt engineering becomes more interesting. 🔹 Step 1 — Start with a Basic Prompt Imagine you're reviewing an application performance report. "Analyze this report and tell me what is causing the performance issues." Copilot may identify a likely cause and provide a recommendation. Sounds useful. But should we immediately accept it? Not necessarily. 🔹 Step 2 — Challenge the Response with 6W + E Instead of asking Copilot for another answer, ask it to validate its own reasoning. WHO — Review the analysis as a cloud solution architect WHAT — Identify the assumptions behind the proposed root cause WHY — Determine whether the evidence actually supports the conclusion WHEN — Consider the performance data across the entire reporting period WHERE — Check whether the issue occurs across all environments or only specific workloads WHICH — Compare the top three possible root causes E — Evidence — Cite the specific data points supporting and contradicting each hypothesis 🔹 Step 3 — Compare the Output Now the conversation changes. Instead of: "This is the problem." We want Copilot to say: "These are the possible causes, this is the evidence supporting each one, and these are the assumptions that still need validation." That's a much more useful enterprise AI interaction. 🔹 Step 4 — EVALUATE Now evaluate the response. Ask: What assumptions did Copilot make? What evidence supports the conclusion? What evidence contradicts it? Are there alternative explanations? What information is still missing? This is an important shift: Don't evaluate only the answer. Evaluate the reasoning behind the answer. 🔹 Step 5 — REFINE Take it one step further: "Based on your analysis, identify the assumptions with the highest risk of being wrong. For each assumption, explain what additional data would validate or invalidate it. Do not make a recommendation until the evidence is sufficient." Now we're asking Copilot to move from: Answer generation → Evidence validation → Decision support 💡 My takeaway The goal of enterprise prompting isn't to make AI sound confident. It's to make AI show its reasoning, expose assumptions and identify evidence gaps. That's where frameworks like 6W + E can become valuable. Prompt → Analyze → Challenge → Validate → Refine And this leads to an important question: When you use Copilot for business or architecture decisions, do you validate the AI's assumptions—or mainly evaluate the final answer? I'd be interested to hear how others are approaching this. #MicrosoftCopilot #GenerativeAI #PromptEngineering #AITransformation #MicrosoftCommunity #EnterpriseAI #CloudArchitecture #AI #CopilotTuesday 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.Tuesday 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.Tuesday Prompt Day | 6W + E Practical Experiment #3 — From AI Output to Business Decision
In our previous discussion, we explored an important idea: Prompt → Output → Evaluate → Refine → Better Output Today, let's take the next step. What happens when the goal is not simply to get a better answer from Copilot, but to get an answer that helps someone make a better decision? Let's look at a practical enterprise scenario. BASIC PROMPT "Review this project update and tell me if we are on track." It looks simple. But what does "on track" actually mean? On track against what? Who needs the answer? What decision are they trying to make? What evidence should Copilot consider? This is where 6W + E becomes useful. 6W + E PROMPT "Act as an enterprise program advisor. Review the project status information provided below and prepare an assessment for the project steering committee. WHY: The purpose is to determine whether the project is on track and whether leadership intervention is required. WHAT: Assess progress, major risks, dependencies, issues and upcoming milestones. WHO: The audience is senior business and IT leadership. WITH: Use only the information provided in the project status material. Do not invent missing facts. WAY: Present the response using these sections: Overall status Evidence supporting the status Key risks and their business impact Critical dependencies Decisions or actions required from leadership WIN: The output should allow a steering committee member to understand the situation quickly and identify where action is required. EVALUATE: Before finalizing the response, check whether each conclusion is supported by the source material. Clearly distinguish facts, observations and assumptions." Notice what changed. The prompt is not simply longer. The problem has become clearer. IMPROVED OUTPUT Instead of simply saying: "The project appears to be on track, although there are some risks." Copilot can be guided toward something more useful: Overall status: Amber - progress is continuing, but a dependency may affect the next milestone. Evidence: Current delivery remains aligned with the planned milestone. A key dependency is still unresolved. The available information does not confirm whether the dependency will be resolved before the milestone. Business impact: If the dependency remains unresolved, the next milestone may be delayed. Leadership action: Confirm ownership and resolution date for the dependency. Information gap: The source material does not provide a confirmed resolution date. That is a very different outcome. The AI is no longer just summarizing information. It is helping structure the information around a business decision. NOW EVALUATE Before accepting this output, ask: Are the conclusions supported by evidence? Did Copilot confuse an assumption with a fact? Is the business impact clear? Is the recommended action actually supported by the information? Can a decision-maker understand the situation quickly? What information is still missing? This is where EVALUATE becomes more than a final proofreading step. It becomes a quality-control mechanism. REFINE Suppose our evaluation identifies one problem: The response identifies the dependency, but the leadership action is still too generic. We can refine the instruction: "Refine the leadership action. Do not simply recommend monitoring the dependency. Identify the specific decision, owner or escalation required based only on the available information. If the source material does not provide enough information to identify an owner or decision, explicitly state what information is missing." Now we have another cycle: Prompt → Output → Evaluate → Refine → Better Output And this leads to a broader question. Are we really trying to teach people how to write better prompts? Or are we trying to teach people how to work effectively with AI? I believe there is an important difference. Prompt engineering may start with the prompt. But effective AI collaboration continues through evaluation, judgment and refinement. YOUR TURN Think about a Copilot interaction you use in your day-to-day work. Ask yourself: What decision is the output supposed to support? What evidence should Copilot use? What would make the answer genuinely useful? How would you evaluate the first response? What would you refine if the answer was only almost right? Share your experience without including confidential information. I'm especially interested in examples where Copilot produced a technically correct answer but the answer was not useful for the actual business decision. Those examples can teach us more than perfect prompts. This discussion continues the 6W + E practical experiment series. Please see the Resources section for the previous experiments and the original 6W + E framework. The goal of this series is not simply to create better prompts. It is to explore whether 6W + E can become a repeatable method for working with AI in real-world scenarios. What would you evaluate first in your next Copilot response?Tuesday Prompt Day 🚀 | 6W + E Practical Experiment #2
In our previous practical experiment, we took a simple Copilot request and transformed it using the Six W + E framework. Today, let's focus on the part that can make the biggest difference: E = EVALUATE A common assumption is: Prompt → Copilot → Answer But in real-world enterprise work, I believe the process should be: Prompt → Output → Evaluate → Refine → Better Output Let's continue with the same scenario. 🔹 BASIC PROMPT "Create a summary of our cloud migration project." The response may be reasonable. But before accepting it, let's evaluate it. 🔹 EVALUATE Ask yourself: Did Copilot understand the intended audience? Did it focus on the business objective? Did it distinguish facts from assumptions? Did it surface the risks that actually matter? Can the intended audience act on the result? Suppose the answer is: "Mostly good, but the risks are too generic and the executive summary contains too much technical detail." That feedback is valuable. We now know what needs to change. 🔹 REFINE Instead of starting over, we refine the instruction: "Refine the previous response for senior business and IT leadership. Reduce technical implementation details. Prioritize the most significant business risks. For each risk, provide: Risk • Business impact • Current mitigation • Decision or action required Keep the executive summary concise. Do not introduce information that is not supported by the source material. Clearly identify any information that is unavailable." Now the interaction has changed. We are no longer simply asking Copilot for an answer. We are using the first answer to improve the next instruction. 🔹 IMPROVED OUTPUT The objective is not necessarily to make the prompt longer. The objective is to make the next interaction more precise. That distinction matters. A good prompt can produce a useful first response. But a good evaluation process helps us systematically improve the result. This is why I see EVALUATE as an important part of Six W + E. It creates a feedback loop: Think → Prompt → Output → Evaluate → Refine And this raises an interesting question for enterprise AI adoption: Should we teach people only how to write better prompts? Or should we teach them how to evaluate AI output and refine their interaction with AI? I believe the second capability is just as important. 💡 YOUR TURN Take one prompt you use with Copilot. Run it once. Then evaluate the response before rewriting the prompt. Share: What you originally asked What was missing or incorrect in the response What you changed in your prompt Whether the second result was actually better Please avoid sharing confidential or sensitive information. I'm particularly interested in examples where the first Copilot response looked correct but wasn't actually useful for the business problem. Those are often the most interesting examples. 🔗 This discussion continues our Six W + E journey. Start with the original framework discussion and then explore the practical experiment series from there. I'll use the strongest examples from this series to explore how Six W + E can evolve from a prompting framework into a practical method for working with AI.Tuesday Prompt Day 🚀 | 6W + E — Practical Experiment #1
Last Tuesday, I introduced a simple principle I’ve been developing for better AI prompting: WHY → WHAT → WHO → WITH → WAY → WIN → EVALUATE 6W + E. 🔗 If you missed the original discussion: https://techcommunity.microsoft.com/discussions/6b6b9aaa-f41d-42fa-b90a-e1bb1d97a954/is-your-ai-prompt-missing-the-real-problem--introducing-the-6w--e-framework/4546006 Today, I don't want to explain the framework again. I want to test it. Let’s take a common Copilot request: “Create a summary of our cloud migration project.” Seems simple. But before asking Copilot to produce the answer, let's think about the problem. WHY are we creating the summary? WHAT exactly should it communicate? WHO will read it? WITH what information should Copilot work? WAY should the information be presented? WIN — what would make the result successful? And finally: EVALUATE — did Copilot actually give us what we needed? Now compare that with a more intentional prompt: “You are an enterprise cloud solution architect preparing an executive update. Create a concise summary of our cloud migration project for senior business and IT leadership. The objective is to communicate progress, business impact, key risks and the next priorities. Focus on the current quarter. Structure the response into: Executive summary • Business impact • Key achievements • Current risks • Next priorities • Decisions required from leadership Keep the language business-friendly and avoid unnecessary technical detail. Where information is missing, clearly identify the gap rather than inventing details.” The interesting part isn't simply that the second prompt is longer. The interesting part is that we have given Copilot a clearer way to understand the problem. And this brings us back to the final part of 6W + E: E = EVALUATE. I don't believe good prompting ends when Copilot gives us an answer. The real cycle is: Think → Prompt → Evaluate → Refine Sometimes the first response is good. Sometimes it isn't. Sometimes the problem isn't the AI's capability. Sometimes we haven't given AI enough direction to solve the right problem. So, here's today's community challenge 👇 Take ONE prompt you regularly use with Copilot. Don't share anything confidential. Share: Your original prompt What you wanted Copilot to achieve Which part of 6W + E was missing How you would improve the prompt Let's see whether we can improve real-world Copilot interactions together. I'll use the best examples from this discussion as we continue developing the 6W + E learning series. And this is only Experiment #1. Next, we'll look at what happens when we deliberately use EVALUATE to improve the first response. What has been your experience? Do you usually refine your Copilot response, or accept the first answer? #MicrosoftCopilot #GenerativeAI #PromptEngineering #MicrosoftCommunity #EnterpriseAI #AITransformation🚀 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 productivity