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From prompts to products: turning GenAI experiments into shipped features
Notes on moving beyond demos, choosing evaluation metrics that matter, and building the operational muscle to ship AI features safely.
Every GenAI project starts as a demo. The interesting work begins when you have to make it reliable, observable, and cheap enough to run at scale.
The teams that ship successfully treat evaluation like a first-class product surface: golden datasets, offline scores, online guardrails, and a feedback loop back into prompts, retrieval, and fine-tuning.
Product managers own the definition of 'good enough' — the threshold above which the model's output is preferable to the current workflow. Without that number, you cannot ship.
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