Enhanced Question Answering via Multi-Stage Search, AI Re-Ranking and Adaptive Queries

The rapid advancement of artificial intelligence and information retrieval has intensified the need for systems that can deliver accurate and contextually relevant answers to complex user queries. Conventional question-answering models often depend solely on pre-trained knowledge, which can lead to outdated or factually inconsistent responses. To overcome these shortcomings, this project introduces an Enhanced Question Answering Framework that integrates Multi-Stage Search, AI-Based Re-Ranking, and Adaptive Query Mechanisms. The proposed system employs a hybrid retrieval process combining dense and sparse search methods to maximize information coverage from large-scale and heterogeneous data sources. Retrieved results are then refined through an intelligent neural re-ranking model that prioritizes semantically relevant content, followed by adaptive prompt construction to minimize noise and improve response accuracy. The integration of these components ensures precise evidence selection, reduces hallucination, and enhances factual reliability and explainability. Experimental analysis on open- domain and multi-hop reasoning datasets demonstrate improved exact match and F1 scores compared to baseline retrieval- augmented models. The proposed architecture also shows robustness across varied query complexities and dataset domains, maintaining consistent performance even under ambiguous input conditions. This framework establishes a more transparent, efficient, and trustworthy approach for building advanced, data- driven, and domain-adaptive question-answering systems suit- able for academic, enterprise, and research applications while promoting interpretability and factual integrity in real-world deployments.