Before you describe what you want, tell Claude who it is. This isn't a magic trick — it's a fundamental shift in how the model frames its response. A role assignment primes the model to draw on domain-specific knowledge, vocabulary, and conventions appropriate to that role.
Without role: "Write a cold email to a VP of Sales."
With role: "You are a B2B sales copywriter specializing in cold outreach for enterprise software companies. Write a cold email to a VP of Sales..."
Large language models are trained on vast amounts of text across thousands of domains. When you assign a role, you're effectively telling the model which subset of that knowledge to draw from. A prompt that starts with "You are a senior negotiation strategist" produces different output than the same request without that framing — the role-assigned output tends to be more specific, more tactical, and more consistent with how a real expert in that domain would approach the problem.
For sales tasks, the most effective role assignments are specific rather than generic. "You are a sales rep" is less effective than "You are a senior enterprise account executive with 15 years of experience selling complex B2B software to Fortune 500 companies." The more specific the role, the more calibrated the output.
The Promptifi prompts all include role assignments — this is one of the reasons they produce better outputs than freeform requests on the same task.