Enhancing Large Language Model Performance on ENEM Math Questions Using Retrieval-Augmented Generation

In this study, we explore the use of Retrieval-Augmented Generation (RAG) to improve the performance of large language models (LLMs), such as GPT-3.5 Turbo and GPT-4o, in solving ENEM mathematics questions. Our experiments demonstrate that RAG potentially provides significant improvements in accuracy by introducing relevant contextual information. With RAG, GPT-4o consistently outperforms GPT-3.5 Turbo, underscoring the potential of this technique to enhance educational AI tools. This research illustrates the potential of RAG-enhanced LLMs to advance educational applications and encourages further exploration in this field.