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.

The critical lack of structured terminological data for South Africa’s official languages hampers progress in multilingual NLP, despite the existence of numerous government and academic terminology lists. These valuable assets remain fragmented and locked in non-machine-readable formats, rendering them unusable for computational research and development. Mafoko addresses this challenge by systematically aggregating, cleaning, and standardising these scattered resources into open, interoperable datasets. We introduce the foundational Mafoko dataset, released under the equitable, Africa-centered NOODL framework. To demonstrate its immediate utility, we integrate the terminology into a Retrieval-Augmented Generation (RAG) pipeline. Experiments show substantial improvements in the accuracy and domain-specific consistency of English-to-Tshivenda machine translation for large language models. Mafoko provides a scalable foundation for developing robust and equitable NLP technologies, ensuring South Africa’s rich linguistic diversity is represented in the digital age.