Information retrieval is an essential aspect in many parts of human life through careers, health, economics, politics, and even through general usage. One commonality of each of these is education. With the rise in artificial intelligence over the past few years, this is even more true as many seek AI to support their educational needs. AI agents leverage large language models to improve this part of our lives, but how information is collected for the individual becomes a challenge. Each person has their own set of priorities and needs, especially within an educational environment and how that data is gathered and presented becomes important, especially for the unique individual. This paper will illustrate how Retrieval-Augmented Generation (RAG) combined with advanced prompt engineering can develop highly personalized and ethical educational experiences. We’ll demonstrate techniques for retrieving highly relevant, contextual, and accurate responses correlating to a user’s prompt. Moreover, we’ll also address mechanisms for improving user prompts to enable more in-depth responses. Our results show the capabilities of advanced retrieval agents fostering more effective, ethical, and robust learning environments for a learner’s education needs.