Prompting is a communication skill before it is an AI skill

Better prompts do not start with magic words. They start with clear purpose, useful context, explicit expectations and a shared definition of success.

People often approach prompting as though it were a special language for talking to machines. They search for the right formula, the perfect opening phrase or a hidden combination of instructions that will unlock a better result.

But many prompting problems begin before the prompt is written. The instruction is unclear because the person giving it has not yet decided what they actually need.

Prompting begins as communication

Consider an instruction such as:

Make this presentation better.

A colleague would probably ask several questions: Better for whom? What is the presentation trying to achieve? Which parts are currently weak? Should it become shorter, more persuasive, more technical or easier to understand?

An AI system needs the same kind of clarity. It cannot read the intention behind a vague request. It works with the objective, context and expectations that have been made explicit.

The quality of a prompt therefore depends partly on the model, but also on the quality of the thinking behind the instruction.

Four communication habits that improve prompts

You do not need a complicated prompting formula for every task. Four ordinary communication habits are often enough to improve the result.

1. Clarify the purpose

Start with the reason the output is needed.

Are you trying to:

• explain a decision;
• persuade an audience;
• compare alternatives;
• identify risks;
• prepare for a meeting;
• turn information into an action plan?

The same source material may require a very different answer depending on the purpose. "Summarize this report" describes an activity. "Summarize this report so the leadership team can decide whether to fund the project" describes the purpose behind it.

That distinction changes what the AI should prioritize.

2. Provide the context that could change the answer

Context is not everything you know. It is the information that could materially affect the result.

That may include:

• who the audience is;
• what they already know;
• what decision has already been made;
• which constraints exist;
• what happened previously;
• which terminology the organiszation uses;
• what should not be assumed.

Without that context, the model may produce something generally reasonable but poorly suited to the real situation.

3. Set expectations explicitly

People often know what they dislike only after they see the first result:

• too formal;
• too generic;
• too detailed;
• too cautious;
• too technical.

Those reactions reveal expectations that were present but never communicated. A useful prompt makes important expectations visible before the answer is produced.

For example:

• use a direct but constructive tone;
• explain technical terms when first introduced;
• prioritize practical recommendations;
• distinguish facts from assumptions;
• show trade-offs rather than presenting one answer as certain;
• keep the main response under 300 words.

Expectations reduce avoidable revisions.

4. Define what a useful outcome looks like

An AI system can follow an instruction without producing something that helps you complete the task. That is why it is useful to define the outcome.

Ask:

• What should the reader understand afterwards?
• What decision should this support?
• What action should someone be able to take?
• What information must be easy to find?
• How will I judge whether the answer is useful?

The desired outcome is not always the same as the requested format. A table is a format. Understanding which option carries the lowest operational risk is an outcome.

A practical example

Consider this request:

Create a plan for onboarding new customers.

It identifies a task, but most of the important decisions remain unstated.

A clearer version could be:

Purpose: Create a practical onboarding plan that helps new customers reach their first successful outcome quickly.

Context: The customers are medium-sized companies adopting a technical SaaS product. Most users are operational managers rather than developers. The current onboarding process consists of one introductory call and a collection of help articles.

Expectations: Keep the plan realistic for a small customer-success team. Explain technical terms clearly and identify any steps that could be automated.

Useful outcome: Produce a 30-day plan divided into customer actions, internal actions, risks and success indicators. The result should help the team identify what to implement first.

This prompt is not based on a secret technique. It works better because the request has been thought through and communicated clearly.