RC-RAG: A Reliability-Centered Retrieval-Augmented LLM Framework for PHM Question Answering

Large language model (LLM)-based question answering has the potential to improve access to technical knowledge, but hallucinated responses remain a severe reliability risk when answers are generated without sufficient evidential support. In reliability-critical domains such as prognostics and health management (PHM), this risk is particularly concerning, as unsupported information can lead to incorrect maintenance decisions or unsafe actions. However, existing retrieval-augmented generation pipelines often lack explicit control over evidence sufficiency and could produce wrong answers, thereby raising concerns about artificial intelligence (AI) reliability issues. In this article, we propose RC-RAG, a reliability-centered retrieval-augmented generation (RAG) framework for PHM question answering that explicitly incorporates evidence evaluation and verification into the retrieval and generation pipeline. RC-RAG augments PHM literature with document-level meta-knowledge, performs hierarchical document–content retrieval, and applies relevance and evidence assessment with verification-aware answer generation to ensure that answers are produced only when sufficient and consistent evidence is available. To evaluate the framework, we construct a curated PHM data challenge question-answering benchmark (PHMQA) derived from a series of PHM conferences and data challenge competitions, including those organized by the IEEE Reliability Society and the PHM research community. Experimental results show that RC-RAG achieves significantly higher accuracy and precision compared to other state-of-the-art methods.