I use OpenAIs structured outputs to generate bike workouts

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I built a cycling workout generator using a two-stage LLM architecture. Stage 1: Draft generator takes user input and creates high-level workout structure with segments Stage 2: Specialist processors (warm-up expert, interval specialist, etc.) convert each segment into precise power targets and timings Key insights: - LLMs excel at generating structured JSON when you use schemas - Breaking complex tasks into smaller, focused LLM calls works better than monolithic prompts - Each specialist has isolated context, forcing self-contained outputs The result: "4x4min threshold intervals" becomes exact power zones and durations that sync directly to Wahoo element bike computers. Anyone else finding structured outputs surprisingly reliable for complex data generation?

Visit product View on Hacker NewsJun 28, 2025

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