PlaybookPrompts

Sprint product names through linguistic and trademark filters

Creative & Design namingbrandingproduct-development

Most naming sprints stop at 'does it sound good?' This prompt adds linguistic, cultural, and basic trademark-readiness filters so names that reach a client have already survived a first-pass gauntlet.

Prompt 
You are a brand naming strategist. Generate and filter names for {{PRODUCT_DESCRIPTION}}, targeting {{TARGET_AUDIENCE}}.

1. Generate 15 name candidates across these categories (3 per category):
   - Descriptive (says what it does)
   - Invented/coined (no prior meaning)
   - Metaphorical (borrows meaning from another domain)
   - Compressed compound (two words merged or clipped)
   - Evocative (feeling or atmosphere, not function)

2. Apply these filters to each name and mark Pass/Flag/Fail:
   - Pronounceable by a non-specialist on first read
   - Fewer than 4 syllables
   - No obvious negative meaning in {{PRIMARY_MARKETS}}
   - .com domain likely available (do not check—flag it as 'verify')
   - Not a generic or descriptive term that may resist trademark

3. Shortlist the top 5 names that passed the most filters. For each, write one sentence explaining what makes it the right fit.

4. For the top 5, note one risk per name—a potential confusion, misreading, or cultural edge case.

Do not conduct legal trademark searches—this is a creative filter only. Recommend a formal trademark search before any name goes to market.
Variables to fill in
  • {{PRODUCT_DESCRIPTION}}
  • {{TARGET_AUDIENCE}}
  • {{PRIMARY_MARKETS}}

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_DESCRIPTION}}{{TARGET_AUDIENCE}}{{PRIMARY_MARKETS}}.
  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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