This study presents FA-RAG, a Retrieval Augmented Generation (RAG) framework leveraging large language models (LLMs) to improve the quality of automated financial advice. The system is constructed from 940 expert-authored financial consultation cases, which include household income and expenditure data, demographic attributes of the consulter, and professional advice from financial planners. Both textual and numerical data are transformed into vector representations and stored in a Pinecone vector database, enabling efficient retrieval of relevant cases. When new consultations are processed, the system queries this database to supply the LLM with contextually relevant information, thereby producing more accurate and personalized advice. For evaluation, FA-RAG was applied to 100 consultation cases, with multiple insurance premium levels examined to analyze how recommended reduction rates vary under different financial conditions. The results demonstrate the potential of integrating domain-specific data and retrieval mechanisms with LLMs to support reliable and tailored financial planning assistance.