In the rapidly evolving landscape of artificial intelligence, the potential of large language models (LLMs) remains a focal point of exploration, especially in the domain of education. This research delves into the capabilities of AI-enhanced chatbots, with a spotlight on the "Teacher Assistant" & "Study Buddy" approaches. The study highlights the role of AI in offering adaptive learning experiences and personalized recommendations. As educational institutions and platforms increasingly turn to AI-driven solutions, understanding the intricacies of how LLMs can be harnessed to create meaningful and accurate educational content becomes paramount.The research adopts a systematic and multi-faceted methodology. At its core, the study investigates the interplay between prompt construction, engineering techniques, and the resulting outputs of the LLM. Two primary methodologies are employed: the application of prompt structuring techniques and the introduction of advanced prompt engineering methods. The former involves a progressive application of techniques like persona and template, aiming to discern their individual and collective impacts on the LLM's outputs. The latter delves into more advanced techniques, such as the few-shot prompt and chain-of-thought prompt, to gauge their influence on the quality and characteristics of the LLM's responses. Complementing these is the "Study Buddy" approach, where curricula from domains like biology, mathematics, and physics are utilized as foundational materials for the experiments.The findings from this research are poised to have significant implications for the future of AI in education. By offering a comprehensive understanding of the variables that influence an LLM's performance, the study paves the way for the development of more refined and effective AI-driven educational tools. As educators and institutions grapple with the challenges of modern education, tools that can generate accurate, relevant, and diverse educational content can be invaluable. This thesis not only contributes to the academic understanding of LLMs and provides practical insights that can shape the future of AI-enhanced education, but as education continues to evolve, the findings underscore the need for ongoing exploration and refinement to fully leverage AI's benefits in the educational sector