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Managing AI latency
How to find and cut the time a user or downstream system actually waits on an AI request. Models a request as a critical path (a graph of serial and parallel spans where only the slowest branch of any parallel work counts toward the total) and separates time-to-first-token from the steady pace of decoding that follows it. Covers what spends that time (prompt size, output length, model and serving choices, retrieval, tool and agent steps), the levers that cut it (streaming, model routing, parallelizing independent work, trimming context and output, timeouts and stage budgets), how to benchmark latency honestly instead of decoratively, and how to keep every optimization from quietly degrading quality on the way to being fast.
Questions in this set
- What latency does a user experience?
- What are time to first token and inter-token latency?
- Why do longer prompts increase latency?
- Why are output tokens often the bottleneck?
- How do model and serving choices change latency?
- How should retrieval latency be managed?
- How do tools and agents multiply latency?
- How does streaming change perceived latency?
- When should work move off the synchronous path?
- How do timeouts and stage budgets keep a slow span from eating the whole deadline?
- How do you benchmark latency correctly?
- How do you cut latency without quietly cutting quality?
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