When you ask for the answer, you get the model's single most average take. When you ask for several genuinely different options, you get a spread — and your judgment does what it's best at: picking and combining.
The trick is forcing real variety. "Give me 5 headlines" produces five near-clones. "Give me 5 headlines using 5 different angles: urgency, curiosity, social proof, data, contrarian" produces five actually-different candidates. Name the axes of variation, then pick a winner and iterate on it alone.
Suggest 6 names for a feature that continuously saves work in our design tool (like Figma). Vary them across these axes, one name per row, in a table:
For each: the name, why it works, and one risk (confusing? trademark-y? too cute?). Audience: professional designers. Avoid anything with 'smart' or 'AI' in it.
We're a 5-person B2B startup selling to HR managers; goal is 200 qualified leads this quarter with a $3k budget and one part-time marketer.
Give me 3 lead-generation options (may include webinar, ebook, or anything better). For each: effort in person-days, realistic lead range, time-to-first-lead, and — most important — the scenario where this option fails. Then recommend one and say what evidence would change your recommendation.
Claude follows 'vary across these named axes' instructions faithfully and is good at honest per-option risks. If options still feel samey, add 'options 5 and 6 should be ones a cautious person would never suggest.'