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Use examples, not adjectives, to get the output you want

Two or three real examples beat a paragraph of instructions. Show the edge cases and the refusals, not just the ideal answer.

In this document

"Write professionally but warmly, concise but complete, formal but approachable." Everyone has written a prompt like that, and everyone has been disappointed by it. Those words mean something different to you than to the model, and there is no way for either of you to find out where you disagree.

One example of an email you were happy with settles all of it in ten seconds.

Take examples from real work

The best examples are things you actually sent, kept, or approved. Not idealised versions — the real ones, with the specific level of detail your company uses and the phrasing your customers are used to.

Two or three is usually enough. Beyond about five, returns fall off quickly and you start pinning the output to the surface features of your examples.

Show the awkward ones, not just the good ones

This is where most prompts stop too early. If every example is a clean, ideal case, the model learns to produce clean, ideal answers — including for inputs that deserve something else.

Include:

A useful ratio: for every three good examples, one refusal and one partial.

Show the input as well as the output

An output on its own teaches style. An input-output pair teaches judgement — what to do with this kind of material. Always pair them, and make the inputs as messy as the real ones: the badly formatted note, the email with three questions buried in it.

Be careful what your examples smuggle in

Examples carry more than you intend. Three specific traps:

Length. If all your examples are four paragraphs, you have specified four paragraphs, whatever your instructions say about being concise.

Structure. Examples that all start with a summary line will produce a summary line forever, including where it is silly.

Content. Real examples contain real customer names, prices and personal data. Anonymise before they go into a shared prompt — a skill everyone can open is a document everyone can read.

Where the best examples come from

Your own past work is the obvious source. The better one, and the one almost nobody mines, is what customers actually said — in sales calls, support tickets, complaints, and the questions they ask before buying.

Two rules make this worth doing:

Keep it near-verbatim. The instinct is to tidy a customer's phrasing into something more polished. Resist it: the specificity is the value. "The last tool we tried needed a developer to set it up" is usable in a dozen places. "Ease of implementation was a concern" is worth nothing, because it could have been said about anything by anyone.

Tag where and when it came from. A customer quote from two years ago may describe a problem you have since fixed, and an undated example is one nobody can judge.

In Belgium, note that recording a conversation and then storing and processing the transcript are separate questions. Decide who is told, what is kept and for how long before you build this into a routine — it is a short conversation up front and an awkward one afterwards.

Where to put them

If several people use the same task, the examples belong in the skill, not in each person's chat history. That is the difference between one colleague being good at this and the team being good at it.

Keep them where they can be edited, dated, and reviewed. Examples go stale: a price changes, a policy changes, and an outdated example is worse than none because it is followed confidently.

Testing that they helped

Change the examples and run your test set. It is the only way to know.

The common surprise is that adding a fourth and fifth example changes nothing measurable, while adding the first refusal example changes a lot. Most teams spend their effort on the first kind.

You can ask your own AI assistant about this. Add https://learn.unpyle.com/mcp as a connector in Claude, ChatGPT or Copilot, and it will answer from Unpyle's published guidance rather than from general advice.

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Published by Unpyle, who help teams put AI to work inside their own company. More at unpyle.com.