"Streaming, caching and other ways to make AI feel fast"
There is a version of your AI feature that feels twice as fast and costs nothing extra to run. Streaming, optimistic UI, precomputation and caching are not optimisations — they are decisions about what the user sees while the work happens.
There is a version of your AI feature that feels twice as fast and costs nothing extra to run. The total processing time is identical; the difference is what the user sees while it happens. Most latency work on LLM features is not about making the model faster — you mostly cannot — it is about designing the wait. That is good news, because designing the wait is cheap.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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