Why should you care?

PromptVerse high-stakes classifiers sample 5 completions at temperature 0.7 and majority-vote the label.

See it live — copy this example

Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.

async function classify(text) {
  const votes = [];
  for (let i = 0; i < 5; i++) {
    const out = await llm({ prompt, temperature: 0.7 });
    votes.push(parseLabel(out));
  }
  return majority(votes);
}

What happened?

• Multiple stochastic samples diversify reasoning paths.
• Majority label beats a single unlucky sample on borderline tickets.

Practice next

1. Run one ambiguous ticket 5 times at temp 0.7.
2. Tabulate labels.
3. Majority vote.
4. Weight votes by self-reported confidence.
5. Drop to 3 samples for latency cap.

Remember

Sample + vote improves robustness. Use on borderline cases only. Normalize answers before vote.

Borderline refund

Ticket could be billing or legal.

Outcome: Majority picks billing; single run picked legal.