Self-consistency prompt builder
A single LLM answer can follow one unlucky reasoning path and land on a wrong result. Self-consistency fixes this by sampling several independent chain-of-thought traces at a higher temperature and then voting on the final answer. This tool wraps your question in a step-by-step prompt, fires the chosen number of independent calls to your own provider, and returns the consensus.
How it works
You bring your own OpenAI or Anthropic API key, enter a question, and pick how many traces to run. Each trace is a separate API request at high temperature, so the model explores genuinely different reasoning. The tool extracts the final answer line from each response and applies your voting method — majority vote by default — to pick the consensus. Every individual trace is shown so you can see how strongly the model agreed with itself.
Tips and notes
- Use it for reasoning, not creativity. Self-consistency shines on problems with a single correct answer: arithmetic, logic, extraction. It is the wrong tool for open-ended writing.
- More traces, more stability — at a cost. Each trace is a paid API call. Five traces is the sweet spot for most questions.
- Watch the agreement. If the traces disagree widely, the question is genuinely hard or ambiguous; treat the consensus with caution and consider rephrasing.
- Your key never leaves the browser. Requests go straight to the provider; nothing is stored on a Gera server.