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A novelist asks for ten different sources of conflict that do not rely on a villain, then chooses one that fits the protagonist's existing motivation.
8 AI Brainstorming Workflow Examples
Use these examples to study structure, specificity, tone, and variation. They are demonstrations—not claims about a real person or organization unless the example itself makes that explicit.
Before you copy
What to notice in the examples
A useful AI brainstorming workflow produces varied options without treating generated ideas as facts, narrows them against real goals and constraints, and records why the writer selected or rejected each direction.
- Define the decision or creative problem before prompting.
- Ask for varied categories of options rather than many near-duplicates.
- Evaluate generated ideas against audience, evidence, originality, feasibility, and purpose.
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A researcher asks for possible subquestions around a broad topic, then checks each against available data before refining the research question.
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A marketer asks for positioning angles grouped by customer problem, proof type, and objection rather than requesting generic slogans.
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A student asks for counterarguments to a draft claim and uses them to identify evidence gaps rather than copying the generated prose.
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An author asks for unusual setting constraints, rejects historically inaccurate suggestions, and develops one plausible option through independent research.
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A manager asks for risks that could derail a rollout, then compares the list with the team's actual dependency map.
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A blogger asks for reader questions around a topic and validates them against search data and support emails.
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A speechwriter asks for three structural approaches to the same message, then writes the actual examples and transitions manually.
Keep the underlying decision or pattern, then replace the subject, evidence, relationship, constraints, and tone with details that belong to your situation. If your final line still works after swapping only one noun, it may be too close to the example.