Think of LLMs as distributed systems, not

Short summary

The author argues that building production AI applications is fundamentally a distributed systems problem, not a prompt engineering problem. Once real users arrive, the challenges that matter are queues, caching, retries, batching, streaming, observability, and rate-limit handling — the same problems backend engineers have solved for decades. The LLM is just one component in a larger pipeline, and the engineering wrapped around it is what determines whether an AI app feels reliable and production-grade.

• •AI apps are distributed systems: the LLM is one box in a pipeline of gateways, queues, caches, and workers
• •Queues, caching, batching, and retries are the real differentiators for production-grade AI, not prompt tuning
• •Spend as much time on backend engineering as on LLMs when learning AI development

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