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.🚀 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