Access to reliable mental health information is vital for early help-seeking, yet expanding knowledge bases is resource-intensive and often misaligned with user needs. This results in poor performance of retrieval systems when presented concerns are not covered or expressed in informal or contextualized language. We present an AI-based gap-informed framework for corpus augmentation that authentically identifies underrepresented topics (gaps) by overlaying naturalistic user data such as forum posts in order to prioritize expansions based on coverage and usefulness. In a case study, we compare Directed (gap-informed augmentations) with Non-Directed augmentation (random additions), evaluating the relevance and usefulness of retrieved information across four retrieval-augmented generation (RAG) pipelines. Directed augmentation achieved near-optimal performance with modest expansions--requiring only a 42% increase for Query Transformation, 74% for Reranking and Hierarchical, and 318% for Baseline--to reach ~95% of the performance of an exhaustive reference corpus. In contrast, Non-Directed augmentation required substantially larger and thus practically infeasible expansions to achieve comparable performance (232%, 318%, 403%, and 763%, respectively). These results show that strategically targeted corpus growth can reduce content creation demands while sustaining high retrieval and provision quality, offering a scalable approach for building trusted health information repositories and supporting generative AI applications in high-stakes domains.

翻译：获取可靠的心理健康信息对于早期求助至关重要，然而扩展知识库不仅资源密集，且常与用户需求脱节。当用户提出的问题未被覆盖或以非正式、情境化语言表达时，检索系统往往表现不佳。本文提出一种基于人工智能的缺口感知语料库增强框架，通过叠加论坛帖子等自然用户数据，真实识别代表性不足的主题（缺口），从而依据覆盖度和实用性确定扩展优先级。在一项案例研究中，我们比较了定向（缺口感知增强）与非定向增强（随机添加）方法，评估了四种检索增强生成（RAG）流程中检索信息的相关性和实用性。定向增强仅需适度扩展即可实现接近最优性能——查询转换仅需增加42%，重排序与分层方法需74%，基线方法需318%——即可达到详尽参考语料库约95%的性能水平。相比之下，非定向增强需大幅扩展语料库（分别为232%、318%、403%和763%）才能达到可比性能，这在实践中难以实现。这些结果表明，通过战略性的定向语料库增长，可在维持高检索与供给质量的同时降低内容创建需求，为构建可信赖的健康信息库及支持高风险领域的生成式人工智能应用提供了可扩展的解决方案。