Anna Dixon, an applied research scientist at Dimagi, explains their project funded by the Bill and Melinda Gates Foundation, which aims to use large language models (LLMs) for family planning education in Kenya and Senegal. The project focuses on fine-tuning GPT-4o mini and GPT-4o for health education chatbots in low-resource languages like Sheng, a Swahili-English slang. Initial attempts using zero-shot and few-shot prompting were ineffective, leading to the injection of Sheng sentences as a style guide, which improved language quality but was costly and slow. The team then implemented a machine translation layer to translate English responses into target languages, enhancing modularity and evaluation capabilities. Fine-tuning improved translation quality significantly, with GPT-4o mini's spBLEU score increasing from 22.21 to 65.23. The project is expanding to other languages, showing promising preliminary results in Chihchewa, with fine-tuned models doubling initial scores. The approach demonstrates cost-effective improvements in language model performance for health education in low-resource settings.