Role prompting

Assigning the model a role — editor, tutor, auditor — together with the criteria that role should apply, to set tone and expectations.

Last updated: November 9, 2026 · Markdown version

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How to apply it in prompts

Use this concept to reduce ambiguity and improve consistency. If you need strict formatting, combine it with a JSON guide and a per-model template.

Related terms:System prompt Constraints

Strict JSON Models Packs

What it means

Definition: you tell the model who it is for this task ('You are a code reviewer') so it adopts that role's vocabulary, priorities and level of rigor.
Why it matters: a role steers tone and focus cheaply. It is most useful when it carries criteria — what that person checks, what they ignore, how they report.
How to apply it: pair the role with rules and an output format. 'You are a code reviewer. Check correctness and security first; report each issue with file, line and a fix' works; 'You are a world-class expert' does not.
Common mistake: expecting a role to add knowledge the model does not have. It changes how the answer is framed, not what the model knows.

Copy-paste example

Role: You are a strict copy reviewer for a B2B website.
Criteria: clarity first, then specificity (no vague claims), then tone (plain, no hype).
Output: a table with columns Issue | Where | Fix, then a rewritten version.

Review this text:
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Opens the analyzer with the prompt prefilled.

Open in Promptea

FAQ

Does 'act as an expert' improve results?

Rarely on its own. Name the specific role and the criteria it applies; the criteria do the work.

Where should the role go?

First, before the task. In an API, put it in the system prompt together with the stable rules.