This presentation explores the use of Prompt-Tuning (PT) to improve brand and language consistency in localization by teaching Large Language Models (LLMs) to develop and apply style guides from minimal examples. PT allows for the automatic enforcement of style guides for specific projects, potentially enhancing translation quality across varied tasks. Our approach involves defining key style guide components such as domain, audience, and formatting standards for acronyms, dates, and measurements, and creating prompts that instruct LLMs to extract and apply these standards in new translation tasks. We conducted extensive tests to evaluate the effectiveness of PT, documenting the process to ensure replicability. The expected results include improved consistency and translation performance, advancing the use of AI in localization and setting a foundation for future innovation in the field.