Retrieval-Augmented Generation (RAG) couples a retriever with a large language model (LLM) to ground generated responses in external evidence. While this framework enhances factuality and domain adaptability, it faces a key bottleneck: balancing retrieval recall with limited LLM context. Retrieving too few passages risks missing critical context, while retrieving too many overwhelms the prompt window, diluting relevance and increasing cost. We propose a two-stage retrieval pipeline that integrates LLM-driven query expansion to improve candidate recall and FlashRank, a fast marginal-utility reranker that dynamically selects an optimal subset of evidence under a token budget. FlashRank models document utility as a weighted combination of relevance, novelty, brevity, and cross-encoder evidence. Together, these modules form a generalizable solution that increases answer accuracy, faithfulness, and computational efficiency.