Reinforcement Fine-Tuning (RFT) was applied to transform a general-purpose code language model, Qwen2.5-32B-Coder, into a domain-specific expert, achieving a 2x improvement in API call accuracy, particularly for complex Stripe API integrations. This approach addresses the limitations of large language models (LLMs) like GPT or Code LLaMA, which often struggle with outdated information, hallucinated methods, and misinterpretations in high-stakes coding tasks. By leveraging Predibase’s fine-tuning platform and Runloop’s Devboxes, the team created a benchmark for evaluating the model's performance, using scoring functions to ensure accuracy in API integration tasks. The process showed that even with as few as 10 prompts, significant gains in model performance are possible, paving the way for domain-specific AI coding assistants that are more reliable and efficient than general-purpose models. This methodology not only improves coding accuracy but also protects sensitive data by allowing developers to maintain ownership of their models and data.