Image exposure correction enhances images captured under diverse real-world conditions by addressing issues of under- and over-exposure, which can result in the loss of critical details and hinder content recognition. While significant advancements have been made, current methods often fail to achieve optimal feature learning for effective correction.To overcome these challenges, we propose Exposure-slot, a novel framework that integrates a prompt-based slot-in-slot attention mechanism to cluster exposed feature regions and learn exposure-centric features for each cluster. By extending the Slot Attention algorithm with a hierarchical structure, our approach progressively clusters features, enabling precise and region-aware correction. In particular, learnable prompts tailored to exposure characteristics of slots further enhance feature quality, adapting dynamically to varying conditions. Our method delivers superior performance on benchmark datasets, surpassing the current state-of-the-art with a PSNR improvement of over 1.85 dB on the SICE dataset and 0.4 dB on the LCDP dataset, thereby establishing a new benchmark for multi-exposure correction. The source code will be available upon publication.