This paper investigates the use of large language models (LLMs) in analyzing and answering questions related to banking supervisory regulation concerning reporting obligations. We introduce a multi-step prompt construction method that enhances the context provided to the LLM, resulting in more precise and informative answers. This multi-step approach is compared with a standard “zero-shot” approach, which lacks context enrichment. To assess the quality of the generated responses, we utilize an LLM Evaluator. Our findings indicate that the multi-step approach significantly outperforms the zero-shot method, producing more comprehensive and accurate responses.