Rapid Naming Rounds
Generate, select, and refine hundreds of names through short feedback cycles
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 6
- Confidence
- 99%
Rapid Naming Rounds replace a single request for names with a high-volume feedback loop. First define the object, audience, context, and broad naming criteria. The AI then generates a numbered set of approximately 20 candidates using a controlled mix of methods, such as descriptive, compound, blended, foreign, or evocative names. The decision maker selects promising numbers and supplies concise feedback about what feels wrong, including hidden requirements discovered during review. That signal changes the next batch, allowing unwanted patterns to diminish and useful directions to expand. Presenting candidates compactly and accepting numeric selections lowers the friction of processing hundreds or even a thousand options. The framework ends with a shortlist, not a final answer: customer research and practical availability checks remain necessary before adoption.
Origin
Jason Keith adapted the breadth and feedback rounds used in professional rebrands into an AI prompt. He contrasted the resulting hour-scale workflow with a previous naming project that required roughly 30 to 40 hours over two months.
Core principles
- 01Naming quality requires breadth of consideration
- 02Short rounds make large searches cognitively manageable
- 03Selection signals should shape every subsequent round
- 04Explicit exclusions prevent AI naming defaults from dominating
How to run it
- 1
Set the Naming Brief
State what is being named, the audience, desired association, market context, and any required words or structures.
Pro tip Explain how a person encountering the name in isolation should interpret it.
Watch out If contextual meaning matters but is unstated, attractive yet irrelevant names will dominate.
- 2
Control the Naming Mix
Specify which naming methods may be used and cap patterns you do not want, such as compounds or blended words.
Pro tip Begin with a varied mix if you do not yet know your preference.
Watch out AI tends to overproduce accepted naming patterns unless instructed otherwise.
- 3
Generate a Compact Round
Request 20 numbered candidates in a compact layout that can be scanned at once.
Pro tip Use numbers so selections can be returned without retyping every name.
Watch out Huge unstructured lists create fatigue and reduce the quality of judgment.
- 4
Select and Diagnose
Choose any promising candidates and explain the most important reasons the rejected set failed.
Pro tip Feedback such as needing a clearer market connection is more useful than merely saying none are good.
Watch out Selecting weak options just to keep the process moving can steer later rounds in the wrong direction.
- 5
Iterate at Speed
Generate another round using the accumulated selections, exclusions, and contextual feedback. Continue for as many rounds as necessary to create genuine breadth.
Pro tip Let repeatedly rejected naming categories disappear from later rounds.
Watch out Do not stop at the first adequate candidate when the decision has long-term brand consequences.
- 6
Validate the Shortlist
Take the strongest candidates to customers or relevant stakeholders and perform trademark, language, domain, and confusion checks.
Pro tip Ask what each name suggests before explaining its intended meaning.
Watch out Internal preference alone does not prove that the market will understand the name.
In the wild
A marketer asks for a name for a post-AI marketing approach. The first round overuses blended words, so he bans made-up combinations. Later rounds produce attractive phrases that lack marketing context, prompting a new requirement that the concept remain recognizable on a book cover. After several sets of 20, he forms a shortlist and tests it with marketers.
→ The search moves from generic AI defaults toward names that communicate the intended category clearly.
Common mistakes
Accepting the Compound-Word Trap
Without controls, the AI may repeatedly generate compound or blended names simply because those patterns are common in naming data.
Giving No Diagnostic Feedback
Rejecting every round without explaining why deprives the system of the signal needed to change direction.
Skipping Market Validation
A compelling internal shortlist still requires customer response and practical clearance before use.
Is it for you?
Best for
It is best for naming products, companies, categories, newsletters, campaigns, or concepts when the decision criteria are partly tacit.
Not ideal for
It is not ideal for final approval without customer research, trademark review, domain checks, and stakeholder alignment.
From the transcript
“So this is designed to give you a variety of those types. It's designed not to fall into certain traps.”
“Like if you're doing a rebrand or naming a new company, you really want like a thousand names in the spreadsheet that you're considering because…”
“And the last thing I'll mention here, I think that's very useful is you want quick iteration.”
From the episode
I Tested 5 AI Prompts That Replace Weeks of Creative Work