Using AI for Brainstorming Without Getting Generic Ideas
ALG Team
Everything AI and More · Aug 20, 2026 · 3 min read
Ask an AI model to "brainstorm ideas" and you'll often get a list of safe, obvious options. Getting genuinely useful brainstorming out of a model takes a bit more direction than that.
- Real constraints push a brainstorm past generic, obvious ideas.
- Ask for a large quantity of ideas, then filter down, rather than starting with a short list.
- Push back directly on vague or interchangeable ideas to get a sharper second pass.
Set real constraints
Unconstrained brainstorming tends toward the most common, expected ideas. Adding real constraints — budget, audience, what's already been tried — pushes the model past the obvious answers toward more specific, useful ones.
Ask for quantity, then filter
Requesting a large number of ideas (twenty, thirty) rather than five tends to push past the first handful of predictable options into more varied, less obvious territory — you can filter the list down afterward rather than starting with a narrow one.
Push back on generic entries directly
If several ideas feel interchangeable or vague, say so and ask for more specific, differentiated versions — a model will often produce genuinely sharper ideas on a second pass once told the first batch was too generic.
Frequently Asked Questions
Why does AI brainstorming feel generic?
Without real constraints, a model defaults toward the most common, expected ideas — adding specific constraints (budget, audience, what's already been tried) pushes it toward more useful, less obvious ones.
Should I ask for a short or long list when brainstorming with AI?
A longer list tends to push past the first handful of predictable options into more varied territory — you can filter the results down afterward rather than starting with a narrow list.