Self-Consistency

Self-Consistency involves presenting a model with a prompt multiple times to achieve consistent results. This method is particularly useful in complex reasoning tasks where consistency in responses is crucial.

Prompt example

Analyze the following customer feedback for a software company: 'The recent update has caused frequent crashes, leading to work disruption. Please address this issue.' Classify this feedback as 'URGENT' or 'NOT URGENT' using step-by-step reasoning.

Output

1. "The feedback mentions 'frequent crashes' which disrupt work. This is critical for user experience. Classification: URGENT."

2. "While the update causing crashes is a concern, it may not affect all users. Further investigation needed. Classification: NOT URGENT."

3. "Crashes can lead to significant work disruption and user dissatisfaction. Immediate action required. Classification: URGENT."

In this Self-Consistency example, the AI processes the same feedback through different reasoning paths. By generating multiple chains of thought, the AI explores various aspects of the issue. The final classification (e.g., URGENT or NOT URGENT) is determined by the majority result from these iterations, ensuring a more consistent and reliable outcome.