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.