Recent advances in generative AI have enabled the integration of large language models (LLMs) into development environments to assist users in programming, interpreting, and debugging. This paper presents a complete data processing and classification pipeline for a GenAI Chat Assistant embedded in the Arduino Cloud Editor. We propose classifying user queries into distinct intent categories, specifically create code, explain, suggest, and fix errors, to optimize Retrieval-Augmented Generation (RAG) responses. The goal is to improve the relevance and efficiency of RAG by tailoring document retrieval and response generation strategies to the specific user intent, thereby reducing irrelevant content and optimizing token usage in LLM responses.