How to understand prompt patterns for Copilot in Excel

If you’ve used Copilot in Excel and noticed that some prompts work beautifully while others miss the point, the difference usually comes down to how you ask. Copilot responds to structure, not magic phrases, and that’s where prompt patterns come in.

Prompt patterns are simple ways of framing your request so Copilot has the right context and direction. Sometimes you want a quick no-context answer, sometimes you show an example, sometimes you build the request step by step, and sometimes you tell Copilot what not to do. Each pattern nudges Copilot in a different way, and knowing when to use which one gives you much cleaner, more predictable results.

In this post we’ll walk through six essential patterns, from zero-shot to one-shot, chaining, refinement, and even negative prompting, and why they matter for Excel users.

If you want to follow along, download the fictitious equities dataset below and open Copilot in Excel:

Zero-shot prompting

Let’s start with the simplest pattern: zero-shot prompting. This is where you ask Copilot a direct question with little to no setup, almost like you’re talking to a human analyst who already knows your data. It’s fast, it’s lightweight, and it’s great when you just need a first draft or a quick read on a pattern. The downside is that Copilot doesn’t have much to anchor on, so the output can drift, stay generic, or miss the nuance you actually care about.

For example, you might try something like: “Using the EquitySnapshot 3table, give me a brief sector-level valuation overview focusing on differences in P/E and EPS. Keep it to one short paragraph.”

Copilot will give you something serviceable, but it probably won’t be tailored to your model, your definitions, or the exact story you’re trying to tell. Zero-shot is useful for breaking the ice with your data. Just don’t expect precision.

One-shot prompting

One-shot prompting gives Copilot a little more direction by offering a single example of the tone or structure you want. You’re basically saying, “Talk like this.” It’s a simple way to nudge Copilot toward a certain voice without overexplaining or writing out a full template. You still won’t get perfect control over depth or structure, but the output usually feels closer to what you had in mind than a pure zero-shot prompt.

For instance, you might say: “Here’s the style I want: ‘Technology names saw higher multiples supported by strong earnings trends.’ Using the EquitySnapshot table, write a similar short summary of how valuations vary across the major sectors.”

That one example tells Copilot the tone, rhythm, and level of detail you’re aiming for. It won’t lock things down completely, but it does give you a clearer, more consistent starting point.

Multi-shot prompting

Multishot prompting builds on the same idea as one-shot, but with more examples to anchor Copilot’s style, tone, and structure. By giving it two or more samples, you’re tightening the guardrails and showing exactly how you want the summary to read. It takes a little more setup, but the payoff is more consistency. Copilot has a clearer blueprint to follow, and you get output that feels closer to your own writing.

For example:
“Example A: ‘Healthcare displayed resilient earnings with mid-range valuations.’
Example B: ‘Consumer names clustered at the lower end of the valuation range.’
Using the EquitySnapshot table, write a sector-level valuation summary in a similar voice.”

With multiple samples, Copilot can triangulate the tone and pacing you’re after instead of guessing. You give it the vibe and the structure, and it fills in the analysis.

Reasoning prompts

Reasoning prompts ask Copilot to walk through its thinking before it gives you the final answer. Instead of jumping straight to a summary, you tell it to spell out how it’s comparing values, what it’s filtering on, or how it’s ranking things. This is especially useful when you care about accuracy and transparency, or when you want to cut down on hallucinations and vague “handwavey” summaries. The tradeoff is that responses tend to be longer and more detailed, so you may need to skim.

For example: “Using the EquitySnapshot table, walk through how you compare sectors based on P/E and EPS before giving the final summary. Then give me a clean, 3-bullet takeaway.”

Here you’re telling Copilot: first, show your work; second, compress it into something tight and useful. That makes it easier to trust the output and reuse the logic later.

Chained prompts

Chained prompts break a task into a sequence of small, deliberate steps. Instead of asking Copilot for one big deliverable, you guide it through the process: explore the data, choose an angle, then produce the final output. This works really well for complex analysis where you want control at each stage and don’t want Copilot to jump straight to a conclusion you didn’t ask for. It takes a little more time, but the end result is usually cleaner and more aligned with your intent.

For example, you might start with: “Scan the EquitySnapshot table and identify standout valuation patterns.”

Then follow with: “Now suggest 2–3 angles to highlight.”

And finish with: “Now write the summary as three crisp bullets.”

By chaining your prompts, you’re basically project-managing Copilot. Each step narrows the direction until the final answer is exactly what you want.

Negative prompting

Negative prompting is all about setting boundaries. Instead of just telling Copilot what you do want, you also spell out what you don’t want in the answer. This is useful when you need the output to stay descriptive, neutral, or compliant, especially in finance or regulated environments. Copilot tends to drift into advice, predictions, or extra color unless you tell it not to, so negative prompting reins that in.

For example: “Summarize sector-level valuation patterns in the EquitySnapshot table, but keep it strictly descriptive and avoid recommendations or forward-looking statements.”

By defining the “no-go zones,” you help Copilot stay focused on the facts in front of it. It’s a simple pattern, but it makes a big difference when precision and tone really matter.

Conclusion

These patterns aren’t some official taxonomy or set of rigid “prompting laws.” They’re just things you start to notice once you’ve used Copilot enough times to see how it behaves. You’re like won’t sit at your desk with Copilot and think, “Let me craft a multishot prompt today.” You’ll just reach for whatever gets the job done, the same way you do algebra or arithmetic without saying the names of the rules out loud.

Most real prompts end up being blends anyway. Maybe you start zero-shot, then follow up with a chained step, then tack on a quick “don’t give me recommendations” at the end. That’s normal. The point is to build a feel for how Copilot responds to structure, examples, boundaries, and sequencing. Once you get that intuition, prompting stops feeling like “prompt engineering” and more like just…using the tool.

To wrap things up, here’s a quick at-a-glance table summarizing the strengths, drawbacks, and best uses for each pattern:

Use this as a reference, but don’t get hung up on labels. Copilot works best when you treat prompting like any other Excel skill: something that gets smoother the more you practice, test, and tweak.