Define the outcome

Beginner · 5 min · State a checkable outcome before writing the rest of a prompt.

prompting
outcomes

Most weak prompts describe a topic. Strong prompts describe a result. Before you write a single instruction, answer one question: what will exist when this is done? A five-bullet summary? A 120-word email? A ranked list with reasons? That sentence is the highest-leverage line in the whole prompt.

Why outcome-first works

A model generates token by token toward what your words point at. Point at a topic and you get the average of that topic — generic, fluent, useless. Point at a concrete artifact and everything after is shaped toward it. The outcome is not decoration; it is the target.

It also gives you your acceptance test. “Was this good?” becomes checkable: five bullets? Yes or no. Right audience? Yes or no. Iteration gets a ruler.

A shape that works

Outcome sentences tend to share a skeleton: artifact + audience + purpose, in about ten words.

“A three-bullet summary a manager can act on.”
“A polite four-sentence decline, suitable to send as-is.”
“A ranked shortlist of five options with one-line reasons.”

A bad example

Ask: “Write something about our Q3 results.”

You get a generic wall of text about business results — plausible, shapeless, impossible to check. Every extra instruction from here is firefighting.

A better example

Ask: “Using the numbers below, write a five-bullet executive summary of Q3: one metric per bullet with its number, plus one risk bullet. No facts beyond the list.”

Same subject — but with a target. The result has a shape you can verify in ten seconds and fix precisely when one bullet misses.

Why it works

Everything else in prompting — context, constraints, format — hangs off the outcome. Define it first and the rest becomes obvious: which context is relevant, which constraints matter, which format fits. Skip it and you are asking the model to guess what you want, then negotiating.

A practical pairing: state the outcome, then name the decision it feeds. Both “a three-bullet status for the Monday stand-up” and “a two-page note for the budget meeting” get built from the same facts — the outcome tells the system which facts matter. When you state both, the model gets a tie-breaker for every small choice, and you get a test for whether the draft is done.

Before you word the rest of a prompt, what does this lesson say to draft first?

A numbered list of every instruction the model must follow One outcome sentence: the artifact, the audience and the purpose, in about ten words A rich description of the topic so the model has full background

Practice

Which prompt aims at a result?

You have a list of Q3 numbers and a manager who will read whatever you send in under a minute.

Which prompt is more likely to produce something you can use as-is?

Hint

Ask: what will exist when this is done — and could I recognise it?

Transfer

Email: “a reply the client can act on the same day.”
Slides: “a five-slide argument for one decision, not a tour.”
Images: “a portrait for a job ad — professional, neutral background.” The outcome is the use.
Code: “a function that passes these three test cases” — the same move.

Key takeaways

Outcome sentence skeleton: artifact, audience, purpose — about ten words.
Point at a concrete artifact, not a topic; the average of a topic is generic.
The outcome doubles as an acceptance test, which gives iteration a ruler.

Next: Give useful context — the details that change everything downstream. The prompt glossary entry covers the basics.