APromptAura

#ab-testing

Top prompts tagged ab-testing.

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p/marketingAauraNovice4 days agohas example⁂auto-checked

Email newsletter subject line and preview text testing set

Generate a testing set of subject lines and preview text for this newsletter issue about issue_topic. Summary of the actual content: content_summary Do this: 1. Write 6 subject line variants across genuinely different angles (curiosity, direct benefit, specific number/stat, question, contrarian/surprising claim, urgency only if genuinely true), not 6 versions of the same idea reworded. 2. Keep each under character_limit characters, since most clients truncate. 3. No clickbait that isn't backed by the actual content, if the subject promises something the email doesn't deliver, that kills long-term open rates even if it wins this one send, don't do it. 4. Write matching preview text for each (the text that shows after the subject line in the inbox), which should ADD information, not just repeat the subject line in different words. 5. Tell me which one you'd actually run first and why, and which one is riskiest (most likely to overpromise) so I know to double check it against the real content before sending.
Claude#email-marketing#subject-lines#ab-testing#newsletter
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p/marketingAauraNovice4 days agohas example⁂auto-checked

Ad variant generator with a real testing hypothesis

Generate number ad variants for platform promoting product_offer to target_audience, but structure this as an actual test, not just a pile of random copy. Core message/offer to test: core_message Budget/format constraints: format_constraints Do this: 1. Before writing variants, define 3-4 distinct hypotheses about what might drive clicks/conversions here (e.g. 'leading with price beats leading with outcome,' 'urgency beats social proof for this audience,' 'a question headline beats a statement headline'). Name each hypothesis explicitly. 2. Write 2 variants per hypothesis, differing ONLY on the variable being tested (so results are actually attributable), keeping everything else as similar as possible. 3. For each variant, state which hypothesis it tests and what result would confirm or kill that hypothesis. 4. Flag if any variant makes a claim that would need substantiation/compliance review (health claims, guarantees, superlatives) before running. 5. Recommend a minimum sample size or budget split logic for a fair test, in plain terms (not exact statistics, just sound testing logic).
Claude#ad-copy#ab-testing#paid-social#advertising
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p/metaAauraNovice4 days agohas example⁂auto-checked

Systematically A/B two prompt variants instead of eyeballing outputs

I have two versions of a prompt for task_description and I want a rigorous comparison, not a vibes-based 'this one sounds better'. Given both prompt variants and n representative test inputs: 1. Before generating any outputs, state 2-4 concrete criteria you'll judge by, specific to this task (not generic 'quality'), and for each, how you'll detect it in an output (a presence/absence check, a count, a structural property). 2. Run both prompts against each test input (or reason about what each would produce if you can't execute code) and score each output against every criterion, showing your scoring, not just a final verdict. 3. Report per-criterion win/loss/tie counts across all inputs, not just an overall 'variant A is better' impression, since a prompt can win on one axis and lose on another. 4. Flag any input where the two variants produced meaningfully different outputs for a reason unrelated to your criteria (e.g. one variant happened to get a lucky/unlucky example), so I don't over-attribute that difference to the prompt wording itself. 5. Give a final recommendation with the specific tradeoff named, not just a winner declared. Variant A: prompt_a Variant B: prompt_b Test inputs: test_inputs
ClaudeGPT#prompt-testing#evaluation#ab-testing
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