Glossary
Multi-model AI
In short
Using more than one AI model on the same task, instead of relying on a single model for every answer.
No single model is best at everything, and they all have blind spots. Multi-model AI sends the same task to several models so their strengths add up and their individual mistakes stand out instead of going unnoticed.
The payoff is twofold. Quality goes up, because a cross-checked answer is harder to get wrong than one model’s confident guess. And cost can go down, because you can route cheap work to small models and save the expensive ones for where they earn their price.
In LLMWeave
Running several models on one prompt is the core of a weave. You write the task once, the models answer independently, and their outputs get merged or ranked into a single result.
Related terms
LLM orchestration
Coordinating one or more large language models, and the steps around them, to complete a task that a single prompt would not handle well on its own.
Model ensemble
Combining the outputs of several models into one result, so the group performs better than any single member.
Model routing
Sending each task, or each part of a task, to the model best suited for it, rather than using one model for everything.
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