An ablation study is an experiment that removes or disables part of a model, dataset, feature, or training method to measure how much that component contributes to performance.
AI teams use ablation studies to separate meaningful improvements from accidental gains. They are especially useful when documenting why an architecture, prompt strategy, retrieval step, or training choice is necessary.