Limited linguistic inclusivity in public health communication leaves many South African communities underserved, particularly regarding critical information on zoonotic diseases such as rabies. This pilot study addresses this gap by developing and evaluating AI-driven methods for delivering reliable rabies information to Sepedi speakers, a low-resource language group. The study presents a novel, curated Sepedi dataset of 60 question–answer pairs, created through a systematic pipeline: thematic analysis of authoritative English sources guided the synthetic generation of QA pairs, which were then translated and manually verified by a native-speaking expert. This dataset was used to compare two large language models, GPT-4o and Gemini-1.5 Flash, under both base and fine-tuned conditions. Evaluation used a human-centred rubric assessing fluency, accuracy, and cultural appropriateness. The findings reveal a key nuance in applying LLMs to low-resource domains. The base GPT-4o model, with strong foundational multilingual capabilities, outperformed all other configurations, including its own fine-tuned variant.
In contrast, fine-tuning provided a marked improvement for the less capable base Gemini model. This result indicates that fine-tuning can enhance weaker models; its benefits are not universal and may be outweighed by the strong zero-shot performance of state-of-the-art architectures when training data is scarce. The curated Sepedi rabies QA dataset will be released under an open licence to support future work in low-resource public health communication.