Brainstorming and idea expansion (without rambling)

Brainstorming and idea expansion (without rambling)

Why it matters: Brainstorming is useful when it expands options without increasing noise.

Goals

• Generate options with constraints.
• Avoid rambling.
• Select top options with criteria.

Key definitions

• Divergence: Generating many options.
• Convergence: Filtering options using criteria.
• Constraints: Rules that keep brainstorming relevant.

Workflow (step-by-step)

1. Define the decision/problem in one sentence.
2. Add constraints (budget, time, audience, tools).
3. Generate 10 options with short descriptions.
4. Score options with 3 criteria (impact, effort, risk).
5. Pick top 2 and write next steps.

Example (good vs bad)

✅ Good: You produce many options, then filter to 1–2 actionable next steps.

❌ Bad: You generate a long list with no criteria or next actions.

Checklist

• Problem stated.
• Constraints set.
• Options scored.
• Next steps defined.

Metrics / criteria

• Options count >=10; selected options have next actions.
• Measurable: You can say whether the output is correct/complete (pass/fail or a score).
• Effort: You can produce the result in 15–30 minutes using the workflow.
• Clarity: Another person can run your prompt and get a similar outcome.

Common mistakes + fixes

• Pitfall: Idea sprawl. Fix: Limit each option to 2 sentences and add criteria scoring.
• Pitfall: Vague instructions. Fix: Add constraints, examples, and a success checklist.
• Pitfall: One-shot prompting. Fix: Iterate: draft → critique → revise → verify.
• Pitfall: No validation. Fix: Add QA steps, tests, and explicit “what counts as done”.

Student tasks

1. Apply the workflow to one real work task related to brainstorming and idea expansion (without rambling).
2. Create a small artifact: prompt, checklist, rubric, table, draft, or decision note.
3. Run one quality check and record what changed after the check.
4. Write a 3-sentence reflection: what worked, what failed, and what you will reuse.

Useful external sources

• OpenAI prompt engineering guide: https://platform.openai.com/docs/guides/prompt-engineering — Prompt structure, iteration, constraints, and evaluation habits.
• NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework — Risk framing, governance language, and verification mindset.
• OWASP Top 10 for Large Language Model Applications: https://owasp.org/www-project-top-10-for-large-language-model-applications/ — Practical AI safety risks such as prompt injection, leakage, and insecure output handling.
• Google People + AI Guidebook: https://pair.withgoogle.com/guidebook/ — Human-centered AI design, user control, feedback, and failure modes.

Bibliography

• OpenAI prompt engineering guide. https://platform.openai.com/docs/guides/prompt-engineering
• NIST AI Risk Management Framework. https://www.nist.gov/itl/ai-risk-management-framework
• OWASP Top 10 for Large Language Model Applications. https://owasp.org/www-project-top-10-for-large-language-model-applications/
• Google People + AI Guidebook. https://pair.withgoogle.com/guidebook/