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.
Why self-consistency beats a single sample
A chain-of-thought prompt asks the model to reason step-by-step before giving an answer, which significantly improves accuracy on multi-step problems. But even with chain-of-thought, a single sample can choose a wrong reasoning path early and never recover.
Self-consistency exploits the observation that correct reasoning tends to converge: many different valid paths lead to the same right answer, while errors are typically idiosyncratic. By sampling a diverse set of paths at higher temperature and taking the majority vote, you filter out the one-off mistakes that individual samples make. The consensus is therefore more reliable than any single trace would be, without requiring a more expensive model.
When to use self-consistency
Self-consistency works best when:
- The problem has a single correct answer — arithmetic, logical deduction, structured extraction, constrained classification.
- You need higher confidence than a single sample gives and cannot or do not want to use a more powerful model.
- The task is reasoning-heavy but not open-ended. Writing, summarisation, and creative tasks do not benefit, because there is no convergence criterion.
It is less appropriate when:
- Latency matters — five API calls take five times as long as one.
- The question is inherently ambiguous or subjective.
- You need a diverse set of outputs rather than a consensus one.
Reading the agreement score
After the traces run, the tool shows how many of them agreed on the majority answer. A 5-of-5 consensus on a five-trace run is very strong. A 3-of-5 split with two different minority answers suggests the problem is genuinely ambiguous or needs clarification. In that case, rephrase the question to remove the ambiguity and run again — a better-specified question typically converges more strongly.
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.