Large language models are challenging to adapt to new enterprise tasks. Prompting is error-prone and achieves limited quality gains, while fine-tuning requires large amounts of human-labeled data that is not available for most enterprise tasks. Today, we’re introducing a new model tuning method that requires only unlabeled usage data, letting enterprises improve quality and cost for AI using just the data they already have. Our method, Test-time Adaptive Optimization (TAO), leverages test-time c...