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From Idea To Agent In Less Than Ten Minutes | WHOOP Engineering

engineering.prod.whoop.com · 1,361 words · saved by 1 readers

At WHOOP, we've been at the forefront of applied AI not by competing in the model race, but by building on top of it with our unmatched physiological data and domain expertise. While others were still debating the potential of LLMs, we had already spent years understanding how to integrate these models with real-world systems and data pipelines. This early investment paid dividends. By the time LLMs started to become truly powerful, we weren't starting from scratch. We had years of hard-won expertise in prompt engineering, evals, model selection, and most critically, applying AI to physiological insights with robust privacy and security guardrails. In the years we spent building LLM agents, we built all kinds of one-off scripts and tools that could remain flexible while we invent new ways of doing things and allow us to keep up with the gen-ai-model-of-the-month release cadence we were seeing. Integrating new data sources and rearranging logic to provide the right context to the LLMs m

At WHOOP, we've been at the forefront of applied AI not by competing in the model race, but by building on top of it with our unmatched physiological data and domain expertise. While others were still debating the potential of LLMs, we had already spent years understanding how to integrate these models with real-world systems and data pipelines. This early investment paid dividends. By the time LLMs started to become truly powerful, we weren't starting from scratch. We had years of hard-won expertise in prompt engineering, evals, model selection, and most critically, applying AI to physiologic

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