RAG Agent: AI with Answers You Can Trust

Large Language Models (LLMs) have shown impressive skills in understanding and generating natural language. However, they often give incorrect or incomplete answers when relying only on pre-trained knowledge. These issues lower factual accuracy, consistency, and user trust in AI systems. To tackle these challenges, this research introduces a Retrieval-Augmented Generation (RAG) Agent. This AI framework combines LLMs with trusted external knowledge sources. The system uses effective similarity search methods to find relevant context from specific databases or document repositories. It then combines this information to create responses that are backed by citations and take context into account. This mixed approach helps ensure factual correctness, transparency, and reliability for various queries. Additionally, the model includes a trust layer that explains and verifies where each output comes from. This reduces errors and makes the information easier to understand. The proposed RAG Agent shows great promise for improving LLM performance in key areas like education, healthcare, and finance. It aims to provide users with accurate, clear, and explainable AI responses.