Large language models (LLMs) excel in text generation; however, they encounter several challenges, including inaccurate facts, hallucinations, and outdated knowledge. Retrieval-augmented methods address these issues by grounding generation in external corpora. Nonetheless, a critical trade-off exists: sparse retrievers, such as BM25, prioritize lexical exactness but overlook semantic variations, while dense retrievers, like DPR, capture semantic relevance but neglect precise term matching. To address this challenge, we propose Adaptive Retrieval Enhancement (ARE), a novel framework that synergistically integrates sparse and dense retrieval through three key innovations: (1) LLM-driven query expansion, which generates diverse and semantically equivalent questions to broaden the retrieval scope; (2) hybrid fusion, which combines BM25 and DPR scores via a trainable dual BERT ranker; and (3) efficiency optimization, incorporating FAISS indexing and adaptive context truncation. Experimental results demonstrate that our method achieves significant performance improvements on datasets such as TriviaQA, Natural Questions, and WebQuestions. This work not only highlights the potential of integrating multiple retrieval technologies but also offers valuable insights for the design of future question-answering systems.