# Ensemble prompting

**Source:** https://promtable.com/glossary/ensemble-prompting

> Ensemble prompting runs the same task with multiple different prompts (or models) and aggregates the responses — typically majority vote, weighted average, or a final reconciliation model.

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Ensemble prompting runs the same task with multiple different prompts (or models) and aggregates the responses — typically majority vote, weighted average, or a final reconciliation model.

Ensemble prompting is the prompt-level analogue of model ensembling. Use several distinct prompt strategies (CoT, few-shot, persona, constraint) on the same task, then aggregate. The technique improves reliability on hard tasks at the cost of N× tokens. Production in 2026 uses ensembling primarily for high-stakes judging (LLM jury) and for hard reasoning tasks where redundancy is cheaper than failure. Mixture-of-agents is a specific form where each branch is a different model.

## When to use

- High-stakes single-shot answers.
- Hard reasoning where being right matters more than cost.

## Common mistakes

- Ensembling identical prompts — no diversity, no benefit.
- Forgetting to log per-branch outputs — you can't debug an ensemble that's wrong.

## Related terms

- [mixture-of-agents](https://promtable.com/glossary/mixture-of-agents)
- [self-consistency](https://promtable.com/glossary/self-consistency)
- [llm-jury](https://promtable.com/glossary/llm-jury)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/ensemble-prompting
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