This research is primarily concerned with the critical problem of synthesizing a structured Retrieval-Augmented Generation (RAG) system for advanced AI applications in the domain of swimming. As the integration of Artificial Intelligence in sports science matures, its applications in swimming have become increasingly diverse, spanning from real-time technical coaching and talent scouting to comprehensive performance profiling and the dynamic personalization of training periodization. Within this landscape, RAG-based systems represent a pivotal advancement in Large Language Model (LLM) enhanced swimming analysis, as they allow for the grounding of generative outputs in authoritative domain knowledge, thereby ensuring the credibility of AI-generated advice, contextually and technically. Despite this potential, building robust RAG systems using only real-world aquatic data presents significant challenges, including ethical constraints regarding athlete biometrics, and the high cost of manual expert labeling. To address these barriers, we propose a novel generative framework that leverages a multimodal knowledge base gathered across four dimensions: physiological data, physiological literature, kinematic sensor data, and unstructured domain expertise. Our proposed framework utilizes a multi-agent LLM architecture to synthesize a high-fidelity dataset of 1,864 validated "Question-Context-Answer" triplets-drawn from 1,914 drafts evaluated against 12 physiological soundness rules. By providing a structured, synthetic ground truth, this work establishes a foundational benchmark for trustworthy AI in aquatics. The outcomes of this research promise to enhance the reliability of automated coaching and open a plethora of future directions in "Meta-Agent" development and athletic profiling, ultimately bridging the gap between raw data engineering and practical sports science application.

翻译：本研究主要聚焦于为游泳领域高级AI应用构建结构化检索增强生成（RAG）系统的关键问题。随着人工智能在体育科学中的整合日趋成熟，其在游泳领域的应用已涵盖实时技术指导、人才选拔、综合表现分析及训练周期动态个性化等多元场景。在此背景下，基于RAG的系统代表了大型语言模型增强游泳分析的重要进展——其通过将生成式输出锚定于权威领域知识，可确保AI建议在语境和技术层面的可信度。然而，仅依赖真实水域数据构建稳健的RAG系统面临显著挑战，包括运动员生物特征数据的伦理限制与人工专家标注的高昂成本。为突破这些障碍，我们提出一种新型生成框架，该框架整合了横跨四个维度的多模态知识库：生理数据、生理学文献、运动学传感器数据与非结构化领域专业知识。所提出的框架采用多智能体大语言模型架构，基于12项生理合理性规则评估的1,914份草稿，合成了包含1,864个经过验证的"问题-上下文-答案"三元组的高保真数据集。通过提供结构化的合成基准真值，本工作为水上运动领域的可信AI建立了基础性基准。研究成果将提升自动化教练系统的可靠性，并为"元智能体"开发与运动员画像分析开辟广阔前景，最终弥合原始数据工程与体育科学实践应用之间的鸿沟。