Retrieval-Augmented Generation (RAG)-Based Chatbot System

The fast development of Large Language Models (LLMs) has highly improved abilities in natural language processing, but deploying LLMs in high-stakes contexts continues to be problematic because of inaccuracy and hallucinations. In this paper, we describe a Retrieval-Augmented Generation (RAG) based chatbot system that avoids these issues from existing models and ensures all responses are grounded and based on valid knowledge sources. The RAG system builds an architecture that scans specific documents in a domain, derives semantic embeddings that include vectors, and stores their vectors in a FAISS vector database, combining retrieval while ensuring speed and memory efficiency. When a question is passed to the RAG bot, the bot retrieves relevant context passages and contexts to condition a generative language model to yield an accurate answer that is also cited. Our implementation involves a modular pipeline and allows all knowledge to be fresh, without having to retrain the language model. We provide experimental results showing significant improvements in factual accuracy compared to baseline LLMs and improved reductions in hallucination. Conclusion: the system is constructed in a domain-free manner allowing further employment in healthcare, legal, or enterprise settings, where it is important to provide cited and verifiable information. The development of this chatbot-status represents an important milestone toward creating trustworthy Artificial Intelligence (AI) systems that exhibit generative fluency and factual reliability.