This paper investigates the application of large language models (LLMs) in the educational field, specifically focusing on roles like "Teacher Assistant" and "Study Assistant" to enhance personalized and adaptive learning. The significance of integrating AI in educational frameworks is underscored, given the shift towards AI-powered educational tools. The methodology of this research is structured and multifaceted, examining the dynamics between prompt engineering, methodological approaches, and LLM outputs with the help of indexed documents. The study bifurcates its approach into prompt structuring and advanced prompt engineering techniques. Initial investigations revolve around persona and template prompts to evaluate their individual and collective effects on LLM outputs. Advanced techniques, including few-shot and chain-of-thought prompting, are analyzed for their potential to elevate the quality and specificity of LLM responses. The "Study Assistant" aspect of the study involves applying these techniques to educational content across disciplines such as biology, mathematics, and physics. Findings from this research are poised to contribute significantly to the evolution of AI in education, offering insights into the variables that enhance LLM performance. This paper not only enriches the academic discourse on LLMs but also provides actionable insights for the development of sophisticated AI-based educational tools. As the educational landscape continues to evolve, this research underscores the imperative for continuous exploration and refinement in the application of AI to fully realize its benefits in education.