[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-prompt-ensembling::en":3,"gloss-cluster-prompt-ensembling::en":23,"gloss-next-prompt-ensembling::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"prompt-ensembling","prompt-eng","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.","Prompt ensembling runs one task through several different prompts and aggregates the results — majority vote for classification, merging for generation.",null,[11,14,17,20],{"slug":12,"name":13},"llm-as-judge","LLM-as-Judge",{"slug":15,"name":16},"prompt-testing","Prompt Testing",{"slug":18,"name":19},"self-consistency","Self-Consistency",{"slug":21,"name":22},"temperature","Temperature",[24,28,31,34,38,41,44,47,50,53,56,59],{"slug":25,"category":5,"name":26,"updated_at":27},"analogical-prompting","Analogical Prompting","2026-08-24T02:46:37+00:00",{"slug":29,"category":5,"name":30,"updated_at":27},"automatic-prompt-optimization","Automatic Prompt Optimization",{"slug":32,"category":5,"name":33,"updated_at":27},"chain-of-density","Chain of Density (CoD)",{"slug":35,"category":5,"name":36,"updated_at":37},"chain-of-thought-prompting","Chain-of-Thought Prompting","2026-08-24T02:46:36+00:00",{"slug":39,"category":5,"name":40,"updated_at":27},"chain-of-verification","Chain-of-Verification",{"slug":42,"category":5,"name":43,"updated_at":37},"chunking","Chunking",{"slug":45,"category":5,"name":46,"updated_at":37},"constrained-decoding","Constrained Decoding",{"slug":48,"category":5,"name":49,"updated_at":37},"context-stuffing","Context Stuffing",{"slug":51,"category":5,"name":52,"updated_at":37},"delimiter","Delimiter",{"slug":54,"category":5,"name":55,"updated_at":27},"directional-stimulus-prompting","Directional Stimulus Prompting",{"slug":57,"category":5,"name":58,"updated_at":27},"emotion-prompting","Emotion Prompting",{"slug":60,"category":5,"name":61,"updated_at":37},"few-shot-prompting","Few-Shot Prompting"]