Retrieval-Augmented Generation (RAG) pipelines often pass a fixed number of retrieved documents to the generator, even though evidence needs vary by query. We study context selection as a post-retrieval control problem: given the same candidate list, how much evidence should enter the prompt, and in what form? We present Safe Adaptive Context, a lightweight controller that starts with limited evidence and expands when the generated answer appears weak, and TACER, a task-aware extension that routes between compact evidence and safer broader context using retrieval-side and evidence-coverage signals. Across five datasets, two generators, and stronger retriever settings, the controllers substantially reduce provider-reported token use while preserving most Fixed Top-10 answer quality. In a five-dataset, 500-query external-baseline stress test, Safe Adaptive Context retains 99.8% of Fixed Top-10 F1 with 55.0% token reduction, while TACER retains 99.4% with 60.4% token reduction. The contribution is not universal quality improvement, but practical quality-preserving efficiency through task-aware context control.