Large language models (LLMs) increasingly serve as recommendation engines, yet existing preference control work primarily focuses on coarse-grained objectives such as safety, helpfulness, or writing style, while overlooking fine-grained provider preferences, i.e., user preferences over specific brands, platforms, or services. Such preferences are often multi-intent, context-dependent, and dynamically changing, making them difficult to control with prompting, fine-tuning, or activation steering methods. In this paper, we present COPILOT, a plug-and-play, training-free framework. It first decomposes each prompt into sub-tasks with corresponding user-specific provider preference configurations. Then it uses a diffusion language model to draft sub-task responses with hard preference constraints injected at the initial mask, which are finally completed into fluent answers by the target LLM. Experiments across code and text domains on three target LLMs show that COPILOT effectively improves provider preference satisfaction by up to 58.9% over the strongest baselines while reducing irrelevant provider mentions by up to 87.8% to ensure the recommendation quality. This work aims to lay a foundation for LLM recommendations that balance personalized experience with real-world application values.