Prompting: write a useful brief before asking for output

Give the task a shape

Describe what you want produced, who will use it and which source material should govern the answer. Include constraints that affect the result, such as length, tone or required fields. Avoid adding instructions that do not change the intended outcome; a long prompt is not automatically a precise one.

Use an example

Instead of asking ‘summarise this report’, ask for three findings for a product manager, each linked to evidence in the supplied report, followed by unanswered questions. This gives the model a task and gives you a way to review the output. If a format is unfamiliar, provide one short example.

Refine from actual failures

When the answer misses the point, identify the failure before rewriting everything. Was evidence absent, the audience unclear or the output format ambiguous? Change one relevant part and try the same test again. Keep successful prompts with the context they require and a date of review. Prompting is most effective as a small cycle of instruction, observation and correction, rather than a hunt for a magic phrase that works on every model and task.

Sources & notes

AI-assisted editorial content checked against the linked sources.

OpenAI: Prompt engineering

Sources reviewed for the September 2026 launch edition.