Running summarizing tools on a new article

Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation, yet they remain susceptible to hallucinations—generating factually incorrect or ungrounded information. This study presents an enhanced Retrieval-Augmented Generation (RAG) framework integrating FAISS vector database, Sentence-BERT embeddings, and Lang Chain orchestration to mitigate hallucinations and improve response accuracy. The proposed system implements a hybrid retrieval pipeline combining dense vector search with semantic reranking. Experimental evaluation on a curated technical knowledge corpus of 12,500 documents demonstrates significant improvements over baseline GPT-3.5: hallucination rate reduced from 31% to 9% (71% reduction), accuracy improved from 68.4% to 91.2%, and average relevance score increased from 0.72 to 0.94. Response latency remained practical at 2.3±0.4 seconds. The framework achieves F1-score of 0.89, demonstrating robust performance across diverse query types. These results validate RAG as an effective approach for grounding LLM outputs in verifiable knowledge, with significant implications for deploying trustworthy AI systems in engineering and technical domains.