Prompt Ensembling

Prompt ensembling runs the same task through several different prompts — varied wording, formats, personas, or example sets — and then aggregates the outputs, typically by majority vote for classifications or by merging for generation. The idea borrows from classic model ensembling: independent, diverse "voters" cancel out each other's idiosyncratic errors, so the combined answer is more robust and less sensitive to any single prompt's quirks. It differs from self-consistency, which samples multiple reasoning paths from one prompt; here the diversity comes from the prompts themselves. For SaaS builders, it's a way to buy reliability on high-value decisions — content moderation, lead scoring, extraction — where one prompt's blind spots are costly. The obvious cost is that N prompts mean N× the calls, tokens, and latency, so it's usually reserved for offline or batch pipelines rather than real-time UX, or used to build a trusted "silver" dataset for later evaluation. It also complicates your prompt-management surface, since you now maintain and version several variants instead of one.