This Brief shows how similarity across dynamical systems can accelerate data-driven controller and estimator design. Classical data-driven control (DDC) methods avoid explicit modeling but remain plant-specific and require costly re-tuning whenever operating conditions change. We propose two complementary strategies that exploit prior designs and perturbed training classes. First, Meta-DDC reuses controllers tuned on similar systems, and its extension Meta-AutoDDC automates the selection of the reference model. Together they guarantee non-deteriorating performance and improved robustness when designing controllers for unseen but related plants [4, 7]. Second, in-context learning with transformer architectures produces contextual controllers and estimators, trained on perturbed simulations to generalize across entire classes of nonlinear systems without re-training [5]. Experiments on brushless motors and nonlinear process benchmarks confirm faster design, reduced data requirements, and improved robustness. We conclude with perspectives on scalable, similarity-aware control design.