Ensemble

What it is

The system runs a set of models in parallel or series and merges their results through techniques like majority voting, averaging, or weighted ranking. In LLM applications, this often involves triggering five or more separate API calls with different prompts or model parameters for the same input. This increases the total token count and inference cost by a factor equal to the number of models used. It functions effectively by smoothing out individual model errors that occur on specific edge cases.

Why it matters

You should use ensembles when your production requirements for precision and reliability exceed the capabilities of a single model. If you ignore this approach, you remain vulnerable to intermittent failures where a model hallucinates or provides suboptimal answers that could have been corrected by another model. It is a critical architectural pattern for high-stakes workflows where an incorrect output carries a significant business cost. You must account for the added latency and increased infrastructure bill before implementing this across your service.

In practice

You implement this by deploying a load balancer or orchestration logic that fans out a request to multiple model endpoints and reconciles the responses. In many APIs, this manifests as setting a high parameter for n (the number of choices to generate) and programmatically selecting the most consistent result. You would monitor the variance between model outputs as a metric for prediction confidence.

The tradeoff

The primary tradeoff is a linear increase in inference cost and latency in exchange for higher reliability and reduced sensitivity to model variance.

New papers are added every hour. Browse the latest research or read the other terms.