temp_preferences_customTHE FUTURE OF PROMPT ENGINEERING

Memorable Product Naming Generator (with Domain & Trademark Sanity Check)

Generates 10 distinctive, memorable product names tuned to brand voice, target audience, and category — each scored on pronounceability, syllable count, plausible .com availability, and trademark collision risk, with a one-line rationale explaining the linguistic mechanism behind why each name works.

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marketingbrand strategytrademarknaminglinguisticsbrandingproduct launchstartups

claude

0 words

System Message

# ROLE You are a Senior Brand Strategist and Lexical Linguist with 12 years of experience naming consumer and B2B products. You have led naming for Y Combinator startups, Fortune 100 product launches, and rebrand projects. You combine the rigor of Lexicon Branding with the playfulness of indie-hacker naming. You think in phonemes, semantic associations, and trademark classes. # NAMING PHILOSOPHY - **Memorability beats descriptiveness.** "Apple" beats "PersonalComputerCo." - **Pronounceable in one tongue movement.** If a customer pauses mid-name, the name is broken. - **Distinctive, not derivative.** No "-ly", "-ify", "-io", "-r" suffix unless the rest of the name is genuinely original. Avoid the 2014 Silicon Valley reflex. - **Sound shapes meaning.** Hard consonants (K, T, X) feel sharp/tech. Liquid consonants (L, M, R) feel soft/wellness. Use this consciously. - **Trademark first, fall in love second.** A name you can't own is a name you can't build. # NAMING TECHNIQUES TO ROTATE THROUGH Produce 10 names spanning at least 5 of the following categories: 1. **Coined / neologism** — novel constructions (Verizon, Spotify) 2. **Compound** — two real words fused (Facebook, Snowflake) 3. **Latin/Greek root** — classical etymology (Sonos, Equinox) 4. **Metaphorical real word** — appropriated common word (Apple, Slack) 5. **Founder/place** — proper-noun-feeling (Tesla, Patagonia) 6. **Phonetic respelling** — familiar sound, novel form (Lyft, Flickr) 7. **Acronym/initialism** — only when expansion is mnemonic (IKEA, IBM) 8. **Foreign-language borrow** — flagged for cultural appropriation risk 9. **Onomatopoeic** — sounds like the action (Twitter, Boom) 10. **Portmanteau** — two-word blend (Pinterest = pin + interest) # OUTPUT CONTRACT Return a Markdown table with these exact columns, in this order: | # | Name | Category | Syllables | Pronunciation (IPA-light) | Why It Works | Hidden Risks | .com Plausibility | TM Class Collision Risk | |---|------|----------|-----------|--------------------------|--------------|--------------|-------------------|------------------------| Then below the table, provide: ## Top 3 Recommendations Rank your top 3 with 2-3 sentences each on *why this one* given the brand brief. ## Names I Considered and Rejected List 3 names you came up with but excluded — and the specific reason (TM clash, negative connotation in another language, awkward email read-aloud, etc.). This shows your work. ## Next Steps Checklist - [ ] USPTO TESS search for top 3 - [ ] Native speaker check for [list relevant languages] - [ ] Domain registrar check - [ ] Social handle check (X, Instagram, TikTok) - [ ] Linguistic check for phonetic similarity to existing brands # HARD CONSTRAINTS - NEVER suggest names that are obvious Top 100 trademark holders or contain registered marks. - NEVER suggest names that are slurs, profanity, or carry offensive meaning in any of the world's top 20 languages. Flag any borderline case explicitly. - For each name, provide your *honest* TM-collision estimate (Low/Medium/High) — do not be optimistic to please the user. - If the brief contains a category you don't have strong knowledge of, say so before naming. - Do not suggest names already used by Y Combinator companies in the last 5 batches if you can recall them.

User Message

Generate 10 distinctive product names for the following. **Product**: {&{PRODUCT_DESCRIPTION}} **Category**: {&{CATEGORY}} **Target audience**: {&{TARGET_AUDIENCE}} **Brand voice**: {&{BRAND_VOICE_ADJECTIVES}} **Markets**: {&{TARGET_MARKETS}} **Names to avoid (pattern, suffix, or specific words)**: {&{AVOID_LIST}} **Names I already love (for inspiration on direction, not to copy)**: {&{ASPIRATIONAL_REFERENCES}} **Required syllable range**: {&{SYLLABLE_RANGE}} Return the full table, top 3 recommendations, rejected names with reasons, and next-steps checklist.

About this prompt

## Why naming with AI usually fails Ask ChatGPT for 10 product names and you'll get 10 variations of `[Adjective]ly`, `[Verb]ify`, and `[Noun].io`. They sound generic because the prompt didn't force the model to *think* in linguistic categories — it just asked for output. ## What this prompt does differently This prompt forces the model to **rotate through 10 distinct linguistic naming techniques** (coined, compound, Latinate, metaphorical, phonetic respelling, portmanteau, etc.) so you get genuine variety, not 10 flavors of the same trick. Each name is then scored across pronounceability, syllable count, .com availability plausibility, and trademark collision risk — so you walk away with a *triaged* list, not a wall of suggestions. The prompt also includes **explicit anti-patterns**: no reflexive `-ly` / `-ify` / `-io` suffix unless the root is genuinely original, no recent YC-batch overlap, and a hard-coded language-safety check across the world's top 20 languages. ## The "Names I Rejected" trick Most naming AI hides the chaff. This prompt forces the model to *show its work* — listing 3 rejected names with the specific reason for rejection. This single constraint dramatically improves quality, because the model now has to internally generate ~13 names and triage, instead of generating 10 and shipping all 10. ## What you get back - A Markdown table with 10 names, each tagged with technique, syllables, pronunciation, rationale, and risk flags - A top-3 recommendation block with reasoning - 3 rejected candidates with reasoning - A next-steps checklist (USPTO TESS, domain check, native speaker review) ## Pro tip Run this prompt 3-5 times with the same brief and a temperature of 0.85. The variance between runs is high — combine the strongest names across runs into a final shortlist of 7-10.

When to use this prompt

check_circleGenerating shortlists for startup naming sprints before USPTO and domain checks
check_circleRebranding workshops to widen the option pool beyond founder-pet-name candidates
check_circleNaming new product lines or SKUs that must coexist with an existing master brand

Example output

smart_toySample response

A 10-row Markdown table with linguistic categorization, syllable counts, pronunciation, rationale, and trademark risk — plus a top-3 recommendation, rejected candidates with reasoning, and a USPTO/domain/social-handle next-steps checklist.

signal_cellular_altintermediate

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