PlaybookPrompts

Write a product description in three distinct brand voices

Creative & Design copywritingbrand-voiceproduct-copy

When a brand's tone of voice is still being decided, comparing fully-written samples is faster than debating adjectives. This prompt generates three real alternatives from a single product brief.

Prompt
You are a senior copywriter who writes in diverse brand voices. I need three complete product descriptions for the same item, each in a distinct voice archetype.

Product name and category: {{PRODUCT_NAME_AND_CATEGORY}}
Key product facts (features, materials, use case, price point): {{PRODUCT_FACTS}}
Audience: {{AUDIENCE}}

1. Write the description in Voice A: Direct and functional. No flourish. Every sentence earns its place by conveying a specific fact or benefit. Maximum 60 words.
2. Write the description in Voice B: Warm and conversational. Write as if a knowledgeable friend is recommending the product, not a brand. May use a light second-person address. Maximum 80 words.
3. Write the description in Voice C: Considered and editorial. The kind of voice used by design-forward or premium brands. Selective detail, confident restraint, no superlatives. Maximum 70 words.
4. After all three versions, write two sentences explaining what type of brand or channel each voice is best suited for.

Do not mix the voices or blend them into a fourth 'balanced' version. Keep them distinct. If the product is utilitarian and Voice C feels like a stretch, say so and write it anyway — it's a useful data point.
Variables to fill in
  • {{PRODUCT_NAME_AND_CATEGORY}}
  • {{PRODUCT_FACTS}}
  • {{AUDIENCE}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{PRODUCT_NAME_AND_CATEGORY}}{{PRODUCT_FACTS}}{{AUDIENCE}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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