In this thesis, I present the development and implementation of ERIC (Environmental Resource Intelligent Companion), an innovative AI-driven solution I designed to enhance environmental management practices within Amazon's Environmental Assurance and Protection (EAP) department. My thesis focuses on leveraging Large Language Models (LLMs) and advanced prompt engineering techniques to address complex challenges in waste classification and environmental compliance. I introduce two novel frameworks: the Multi-Model, Multi-Document, Mixture of Agents (M3D-MoA) framework for orchestrating multiple LLMs, and the Document Augmentation technique for enhancing In-Context Learning. These innovations address key limitations in current LLM applications, such as context window constraints and resource intensiveness.