RAG ENGINE INFORMATION RETRIEVAL SYSTEM USING LLM

The RAG Engine Information Retrieval System using Large Language Models (LLM) is designed to improve the accuracy and relevance of information retrieval by combining retrieval-based methods with generative AI models. Traditional AI models generate responses based only on pre-trained knowledge, which may lead to outdated or incorrect answers. To overcome this limitation, the proposed system integrates a Retrieval-Augmented Generation (RAG) approach. The system retrieves relevant documents from a knowledge base using vector embeddings and similarity search techniques. These retrieved documents are then provided as context to a Large Language Model, which generates accurate and context-aware responses. The system is developed using modern technologies such as Python, vector databases, and LLM APIs. The RAG engine enhances response accuracy, reduces hallucination, and ensures up-to-date information retrieval. It is scalable, efficient, and suitable for applications such as chatbots, question-answering systems, and knowledge management platforms. In addition, the system is capable of handling complex user queries by understanding semantic meaning rather than relying on simple