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Development teams building LLM applications can improve their model evaluation and rollout practices by shifting A/B testing from the application layer to the inference endpoint, enabling faster iteration on model selection while simultaneously gathering production evidence of user preference—critical for managing the operational and user experience risks of swapping foundation models.
Together AI published guidance on A/B testing LLM models in production using shadow traffic and split testing at the endpoint rather than in application code. The approach allows teams to validate that a candidate model is operationally sound while measuring actual user preference through production traffic splits.
Read the full article at Together AI
CoFabrix summarises and comments on this story. The original reporting belongs to Together AI.
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