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 #CopilotGetting Started with Copilot Studio: Your PAA & FAQ Guide
What is Microsoft Copilot Studio? Microsoft Copilot Studio is a low-code, graphical tool within the Power Platform used for building and conversational bots. It empowers users, even those without extensive technical backgrounds, to create sophisticated logic and connect to various data sources and services using prebuilt or custom plugins. Is Copilot Studio easy to use for beginners? Yes Copilot Studio is designed to be easy for beginners. You only need to describe the agent you want in plain language to start creating it. The platform uses a graphical, low-code interface that streamlines the process of defining instructions, knowledge sources (like documents), and conversation triggers, making it accessible to most users. What is the difference between Microsoft 365 Copilot and Copilot Studio? Microsoft 365 Copilot is an AI assistant that integrates across Microsoft 365 apps (Word, Excel, Teams, etc.) to enhance productivity. Copilot Studio, conversely, is a development platform used to build customised AI agents that are tailored to specific business goals or data sources. Copilot is the agent you use; Copilot Studio is the tool you use to build or extend agents. Do end-users need a specific license to use a Copilot I create? Yes, licensing for end-users depends on how and where the custom copilot is deployed. While development often requires a Power Platform or Azure subscription deploying the bot across an organization may require specific Copilot licenses for the end-users accessing the agent. Check the official Microsoft licensing documentation for your specific scenario. How can I add SharePoint data as a knowledge source for my Copilot? You can connect your Copilot agent to SharePoint data using the generative answers feature in Copilot Studio. The agent can search documents stored in a SharePoint document library. Be aware that there can sometimes be nuances with how attachments versus core document libraries are indexed, which the community is actively discussing in the forums. We hope this formatted FAQ helps you quickly find the information you need! If you have more questions, please use the discussion board to connect with the community. This Blog Post Was Drafted With The Help Of Artificial Intelligence On The Copilot Studio User GroupTuesday 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 #AITransformationGenAI Knowledge Byte | KB-002
Understanding AI Hallucinations: Why AI Sometimes Gets Things Wrong 🤖 Generative AI is incredibly powerful, but it's not always correct. One of the biggest challenges with AI is hallucination—when an AI model generates information that sounds convincing but is actually incorrect, misleading, or completely fabricated. 💡 Why do hallucinations happen? AI predicts the most likely next word based on patterns it learned during training. It doesn't "know" facts the way humans do, so when information is missing or ambiguous, it may confidently generate inaccurate answers. 🚨 Common causes ✅ Ambiguous or incomplete prompts ✅ Outdated training data ✅ Missing business context ✅ Complex reasoning across multiple topics 🛡️ How to reduce AI hallucinations ✔️ Provide clear and specific prompts ✔️ Include relevant context and reference material ✔️ Ask the model to cite sources when appropriate ✔️ Verify important information before making decisions ✔️ Use enterprise AI solutions like Microsoft 365 Copilot, which ground responses in your organization's authorized data while respecting permissions. 💼 Microsoft Perspective Microsoft's Copilot experience combines Large Language Models with enterprise data through grounding techniques, helping improve response relevance while still encouraging users to validate critical outputs. 🎯 Key Takeaway AI is an intelligent assistant—not an infallible expert. The best results come from combining AI with human judgment. 💬 Discussion: Have you ever encountered an AI hallucination? What techniques do you use to verify AI-generated content?🚀 Prompt Tuesday | Write Prompts Like a Pro
Prompt Tuesday | PT-002 | A small change in your prompt can dramatically improve AI responses. Instead of asking: ❌ "Summarize this document." Try this: ✅ "Summarize this document into 5 key points. Highlight risks, action items, and decisions. Keep the response under 200 words and format it as a table." 💡 Prompt Formula Role + Task + Context + Constraints + Output Format Example: Act as a Microsoft Solutions Architect. Review the following Azure migration proposal. Identify technical risks, suggest improvements, and present the findings in a table with Risk, Impact, and Recommendation. Why it works ✔ Gives AI a clear role ✔ Provides context ✔ Defines expectations ✔ Specifies the output format The more specific your prompt, the better the results. 💬 Challenge: Share one prompt that saves you time at work. Let's learn from each other!🧠 What is Retrieval-Augmented Generation (RAG)?
Have you ever wondered how AI tools answer questions using your company's documents instead of making things up? That's where Retrieval-Augmented Generation (RAG) comes in. Instead of relying only on what the AI learned during training, RAG first searches trusted sources—such as PDFs, SharePoint libraries, knowledge bases, or internal documentation—and then uses that information to generate a response. Why organizations use RAG ✅ Reduces hallucinations ✅ Uses the latest company knowledge ✅ Keeps responses grounded in trusted data ✅ Improves enterprise AI accuracy Common Microsoft stack Azure AI Search Azure OpenAI Microsoft Copilot SharePoint Microsoft Fabric RAG is one of the key building blocks behind modern enterprise AI assistants. 💬 Discussion: Have you implemented a RAG solution in your organization, or are you planning one?How Generative AI Learns and Creates 🎨🤖
Today, we will learn and understand how Gen AI actually learns to create new things. Generative AI models learn by studying patterns from massive datasets — such as text, images, or audio. They don’t memorize this data. Instead, they identify how words, shapes, or sounds connect — and then use this understanding to create something new. For instance, when you ask Microsoft Copilot or ChatGPT to write a paragraph, the AI doesn’t copy it from the web. It uses what it has learned from patterns in language to generate fresh, original text. Similarly, image tools like DALL·E create pictures based on descriptions by learning visual structures and textures. In simple terms, Generative AI learns like an artist who studies thousands of styles — then paints something unique. ✨ Try this: Ask Copilot or ChatGPT to “write a two-line poem about teamwork in space.” Observe how it constructs ideas and language. That’s AI creation in action! 💬 Share what you tried — or what surprised you most — in the comments below!Why AI Collaboration Matters More Than AI Tools in 2026
Artificial Intelligence has evolved from being a "nice-to-have" productivity tool to becoming an integral part of how we collaborate, innovate, and solve problems together. But here's something I've been reflecting on: The real competitive advantage isn't having access to AI—it's knowing how to collaborate with AI effectively. Organizations around the world are adopting solutions like Microsoft Copilot, Microsoft 365 Copilot, Copilot Studio, and Azure AI to streamline workflows and unlock productivity. Yet, the teams seeing the greatest success aren't necessarily using the most advanced tools—they're building a culture where people and AI work together. What does AI collaboration look like? It's about using AI to enhance, not replace, human expertise. For example: 💡 Brainstorming ideas with Copilot before a team meeting. 📊 Transforming raw data into meaningful insights with AI assistance. ✍️ Drafting documents faster while applying your own judgment and expertise. 🤝 Sharing prompts, best practices, and lessons learned across teams. 🚀 Automating repetitive tasks so people can focus on creativity, strategy, and innovation. The technology is powerful, but collaboration is what creates real value. Three habits of successful AI-powered teams ✅ Share what works. A great prompt or workflow can save hours for your colleagues. Building a culture of knowledge sharing helps everyone grow together. ✅ Experiment continuously. AI capabilities evolve rapidly. Small experiments often lead to significant productivity improvements. ✅ Keep humans in the loop. AI can generate content and suggestions, but people provide context, critical thinking, and ethical decision-making. A question for the community As AI becomes part of our daily work, what's the biggest change you've noticed in the way you collaborate with your team? Have you found a Microsoft Copilot feature that's transformed your workflow? Has AI changed the way your team communicates or shares knowledge? What's one lesson you've learned from working alongside AI? I'd love to hear your experiences. Your insights could inspire someone else's next productivity breakthrough. How is your team using Microsoft Copilot or other Generative AI tools to improve collaboration? What has worked well, and what challenges have you encountered? Share your experience in the comments.
