A Hybrid Retrieval-Augmented Generation and Language Model Framework for Evidence-Grounded Review Systems

Evidence-grounded review systems require balancing comprehensive knowledge retrieval with accurate and reliable generation. Traditional approaches often struggle with maintaining factual consistency, providing proper attribution, and combining complex multi-source evidence. In this study we propose a reliable hybrid framework that integrates retrieval-augmented generation with large language models to support evidence-grounded critiques, risk assessments, and recommendations. The framework created ensures to incorporate structured rubrics, a dual-model verification, and a human-in-the-loop to enforce and ensure quality control to produce reliable outputs across domains. Unlike prior systems such as Atlas and RETRO, the approach proposed in this research introduces explicit verification and calibration mechanisms that reduce factual errors and improve attribution. Empirical evaluations applied show visible and notable improvements in groundedness (91% vs. 71% baseline), consistency (89% vs. 63% baseline), and reliability (ECE 0.042, 47% lower than Atlas). Our approach uses a browser-based architecture which removes the need for specialised hardware, making the system more accessible. This work advances the development of trustworthy review systems and has broader implications for high-stakes fields such as healthcare, legal analysis, and policy evaluation.