Better AI Prompts Start With Better Constraints

A good prompt is less about finding magic words and more about removing ambiguity. Tell the model what it should do, what it should know, and what a useful answer looks like.

Compare these two requests:

Write about climate change.

Explain the main causes of climate change in 300 words for a secondary-school reader. Separate measured evidence from future projections and list the sources that should be checked.

The second prompt defines an audience, scope, length, and standard for the answer. It does not guarantee accuracy, but it gives you something much easier to review.

A prompt can have five useful parts

You rarely need all five, but they are a good checklist:

1. Task: What should the model do?
2. Context: What information does it need?
3. Constraints: What must it include or avoid?
4. Format: Should the result be prose, JSON, a table, or code?
5. Examples: What does a good input or output look like?

For repeated work, examples are especially useful. They show tone and structure more reliably than a pile of adjectives such as “engaging” and “professional.”

Treat the first answer as a draft

If the result is wrong, identify the failure instead of asking the model to “make it better.” Ask it to shorten the opening, preserve specific terms, expose assumptions, or return missing fields.

For factual work, require sources and then open them yourself. A model can invent a very plausible citation. Confidence is part of the output style, not evidence.

You can practise with text tools such as ChatGPT or image tools such as DALL·E and Midjourney. The interfaces will change, but clear instructions and careful review remain useful.

I would not spend too much time collecting prompt formulas. Start with a real task, make the success criteria explicit, and improve the prompt where the result actually fails.