This project focuses on improving accessibility to legal documents through a Retrieval Augmented Generation (RAG) system. The system allows users to ask natural language questions about terms and services and receive clear, context-aware answers.

Multiple chunking and embedding strategies were explored and compared. The backend used vector search via Pinecone, and the front-end was built with Streamlit for usability and transparency. Beyond development, the project included an evaluation of the overall RAG pipeline measuring relevance, answer accuracy, and user experience across configurations.