This study investigates the application of Language Modelling in cycling aerodynamics. A novel ground truth is created through recruiting a cohort of experts in cycling aerodynamics, bike fit and biomechanics and taking that ground truth to be the collective expert consensus. Within this study 9 Large Language Models and 1 Large Reasoning Model were tested with 7 of the Large Language Models being open-source models from Google, Meta, Microsoft and Alibaba and the closed source models from OpenAI. This study tested these models without a system prompt, with a system prompt, with applied Retrieval Augmented Generation, with an enthusiast level knowledge base and Retrieval Augmented Generation with a more technical knowledgebase. The best performing model in this study was OpenAI`s Chat-GPT 4o with an average mark of ()%. And the best performing opensource model was Alibaba`s Qwen2.5:32b with a system prompt and the technical knowledge base providing an average score of . The results from this study show that it is possible to develop a model which performs to a similar level of a human expert within the domain of aerodynamics, bike fit and biomechanics in cycling. Additionally, this study proposes a method to experimentally quantify the improvements an athlete can make through the assistance of a domain specific Large Language Model.
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