Authors - P.Pandiaraja, N.Shiva Kumar, B.Vishnu Vardhan, C.Sevarathi, Charles Prabu V, S.Jagan Abstract - Retrieval-Augmented Generation (RAG) chatbots represent a significant advancement in intelligent conversational systems, grounded in the prin-ciples of natural communication, accuracy, and reliability. Traditional chatbots are constrained by pre-trained knowledge or rule-based responses, limiting their effectiveness in dynamic and complex real-world scenarios. RAG-based systems integrate information retrieval mechanisms with sophisticated language generation models to identify relevant knowledge in real time and produce contextually appropriate responses. The proposed system employs sentence-transformers (all-MiniLM-L6-v2) for dense vector embeddings and FAISS as the vector data-base backend, enabling fast and semantically accurate document retrieval. Ex-perimen- tal results demonstrate a mean retrieval accuracy of 87.4%, an average response latency of 1.3 s, and a user satisfaction score of 4.2 out of 5, confirm-ing the system’s readiness for real-world deployment.
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