The use of formal methods is a significant contribution to developing trustworthy software; however, it can be a complex task. For this, automation with generative artificial intelligence models, such as Large Language Models (LLMs), is considered a promising approach. We studied the use of LLMs to generate fixes for faulty formal models of the Event-B formalism. To fix faulty Event-B models, we propose a System Prompt that contains constraints on how to suggest fixes that respect the syntax and rules of the Event-B language. We also propose Retry Prompts, a type of prompt that aims to improve a fix suggested by the Large Language Model by pointing out errors in previous responses. To evaluate our method, we developed a tool that generates faulty models (mutants) from pre-existing models by removing a single action or guard predicate. The tool then interacts with an LLM to obtain a suggested fix for the mutant model. After applying the suggestion made by the Large Language Model, we evaluate the state of the new Event-B model accordingly. The results demonstrate that using Retry Prompts significantly increases the success rate of the suggested fixes, with over 80% of the faulty models in our dataset being successfully fixed. The results also indicated directions of possible future improvements, such as combining Generative AI with formal approaches to fix failing cases.