TA-RAG: Tone-Aware Retrieval-Augmented Generation for Peer-Support Health Communication
For developers of health communication systems, this work addresses the need for tone-aware responses in sensitive domains, but the approach is incremental as it applies prompt engineering to RAG without novel methodology.
TA-RAG introduces a prompt-based, fine-tuning-free framework that adds tone control (stigma-free, empathetic, readable, recipient-adapted) to RAG for HIV peer support, improving communication quality while preserving content.
Retrieval-augmented generation (RAG) successfully grounds large language model (LLM) outputs in trusted documents, but factual grounding alone is insufficient for sensitive peer-support health communication. In domains such as HIV peer support, responses must also be accessible, stigma-free, empathetic, and tailored to the recipient. This paper presents TA-RAG, a lightweight, prompt-based tone-aware RAG framework that embeds explicit tone control into a RAG pipeline without requiring model fine-tuning. We operationalise tone across four core components: stigma-free rewriting, readability adjustment, recipient adaptation, and empathy rephrasing. We evaluate TA-RAG through component-level tests using questions derived from HIV Online Learning Australia (HOLA), UNAIDS terminology guidance, readability metrics, peer-support standards from National Association of People with HIV Australia (NAPWHA), and a public empathy dataset. Results show that the TA-RAG's components improve their targeted communication quality while preserving key content. These findings emphasise that prompt-based tone control is a potential direction for making RAG outputs suitable for sensitive peer-support health communication.