Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts

This paper introduces ReElicit, a Bayesian optimization framework based on "embedding by elicitation" for tuning system prompts in AI. It leverages LLMs to elicit an interpretable feature space and a Gaussian process surrogate to select and refine prompts based on aggregate feedback.