The paper treats prompt optimization for retrieval agents as a debugging process rather than blind search. Contrastive reflection compares successful and failed behavior to guide iterative changes.
The work is relevant because agents increasingly issue queries, synthesize answers, and judge retrieval quality. Better prompt optimization can improve these systems without changing the underlying model.