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?