[observationist]

Your explanations are still unclear.

I know what evaluation is, and inference, and training. Deployment means to deploy - to put a model in production. It does not mean inference. Inference means to input a prompt into a model and get the next token, or tokens as the case may be. Training and inference are closely related, since during training, inference is run and the error given by the difference between the prediction and target is backpropagated, etc.

Evaluation is running inference over a suite of tests and comparing the outcomes to some target ideal. An evaluation on the MMLU dataset lets you run inference on zero and few shot prompts to test the knowledge and function acquisition of your model, for example.

So is your code using Bayesian Optimization to select a subset of a corpus, like a small chunk of the MMLU dataset, that is representative of the whole, so you can test on that subset instead of the whole thing?