Self-consistency
Self-consistency is an inference strategy that prompts a model multiple times for the same input and selects the final answer via majority voting.
What it is
The system generates N distinct reasoning paths by setting a non-zero temperature, typically between 0.5 and 0.7. It then parses the outputs to identify the most frequent result among those independent attempts. If the model is asked a math problem, it runs five or ten iterations in parallel or via a queue. The aggregate majority vote provides a more stable answer than a single greedy decoding attempt, effectively smoothing out individual reasoning errors.
Why it matters
This technique significantly reduces the error rate for logic-heavy tasks without requiring model retraining or fine-tuning. Relying on a single inference pass is often insufficient for complex workflows where accuracy is critical. By implementing this, you move from probabilistic single-shot outputs to a more deterministic, ensemble-based result at the cost of higher latency and request volume. Ignoring this leads to brittle features that fail frequently on edge cases.
In practice
In production, you would implement this by setting the temperature parameter above zero and wrapping the model call in a function that collects responses into a collection before calculating the mode. You must handle the increased latency and cost, as you are essentially multiplying your inference budget by N for each call. Observability tools should be configured to track the standard deviation of these outputs to identify when the model is struggling with a specific input.
The tradeoff
The primary tradeoff is a linear increase in inference cost and latency in exchange for higher output reliability and reduced variance.