AI brainstorming prompts: generate and filter ideas without generic filler

How to use AI for brainstorming without getting obvious, repetitive ideas — covering divergent generation, constraint-based prompting, and structured filtering.

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Why AI brainstorming often produces generic ideas

Vague prompts produce safe, central ideas: 'give me ideas for my app' returns the most common suggestions the model has seen — not the most useful for your specific situation.
No diversity requirement means clusters: without asking for variety, the model generates ideas that are similar to each other in angle and approach.
Asking for 'creative' ideas without constraints isn't enough — creativity needs a constraint to push against. 'Creative' alone defaults to novelty-sounding but structurally ordinary ideas.
Mixing generation and evaluation in the same prompt produces filtered, cautious output. AI self-censors during generation when it knows ideas will be judged immediately.
No domain specificity means generic: the model defaults to industry-standard suggestions without knowing your constraints, audience, or context.

Constraints that produce better ideas

Ban the obvious: list 2–3 categories that are too common and explicitly exclude them. 'Don't suggest social sharing features, gamification, or push notifications' forces the model past the defaults.
Force diversity: ask for ideas from different angles — 'one that reduces cost, one that increases speed, one that changes the target user completely.'
Use cross-domain combinations: 'apply the core mechanic of [unrelated domain] to solve [your problem]' produces ideas that feel genuinely different.
Separate generation from evaluation: run one prompt for raw idea generation (quantity, no filtering), then a separate prompt to evaluate and rank. Mixing them cuts quantity and diversity.
Add a constraint as a creative pressure: 'what would this look like if it had to work with zero budget?' or 'what's the minimum version that still solves the core problem?'

Templates

Divergent idea generation with forced diversity

Generate [N] ideas for [problem or opportunity].

Context:
- What it is: [brief description of the product/project/goal]
- Target user: [who this is for]
- Current constraint: [the main limitation — budget, time, technical, etc.]

Diversity requirement — generate at least one idea in each of these directions:
1. One that reduces cost or complexity.
2. One that completely changes the target user or use case.
3. One borrowed from a different industry or domain.
4. One that is the simplest possible version.
5. [Optional: add your own angle]

Do NOT suggest: [list 2-3 obvious ideas to exclude]

Format: numbered list. For each idea: one-line description + why it fits the constraint.

Do not evaluate or recommend yet — just generate.

Opens on home with the prompt prefilled.

Open in Promptea

Idea evaluation and filtering

Evaluate these ideas and help me prioritize them.

Ideas:
[paste your list of ideas here]

Evaluation criteria (most important first):
1. [Criterion 1, e.g. "feasibility with a 3-person team in 2 weeks"]
2. [Criterion 2, e.g. "solves the core user pain, not just a nice-to-have"]
3. [Criterion 3, e.g. "low reversibility risk — easy to undo if it doesn't work"]

For each idea, return:
- Score: [1-5] for each criterion
- Strongest point: [what makes this idea worth considering]
- Biggest risk: [the main reason it could fail]
- Verdict: [Keep / Develop further / Drop]

End with:
- Top 2 ideas to develop further and why.
- One idea that looks weak but might be worth revisiting with a different constraint.

Opens on home with the prompt prefilled.

Open in Promptea

FAQ

How do I get AI to generate genuinely original ideas?

There's no guarantee of true originality, but you can push toward less common territory by banning the obvious categories, asking for cross-domain combinations, forcing diversity requirements, and adding unusual constraints. Originality also depends on your follow-up: use AI-generated ideas as starting points, not final answers. The value is in covering more ground quickly so you can identify the non-obvious angles worth exploring.

Should I use AI for all my brainstorming?

AI is best for quantity and breadth: quickly covering the idea space and surfacing angles you might not have considered. Use it early in a process to generate a large set of candidates. Human judgment remains essential for filtering — you understand context, stakeholder dynamics, and implementation realities better than the model. A good workflow: AI for generation, you for filtering and developing the best candidates.