OpenAI's prompt caching update for GPT-6 sounds like a developer feature, but the real story is cost control. Better cache hit rates, diagnostics, explicit breakpoints, and controls are the kind of details that determine whether AI workflows are affordable at scale.
As AI applications move from chat boxes to long-context agents, repeated prompts become a tax on latency and spend. Caching is one of the infrastructure levers that lets teams reuse stable context instead of paying to reprocess it on every call.
For engineering teams, this is a practical signal: model choice is no longer enough. The teams that win will understand caching, routing, context layout, observability, and cost behavior as part of the product architecture.
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