Find the AI latency bottleneck before changing models
Measure queueing, retrieval, first output and completion separately, then reduce the stage that is actually delaying the task.
In brief
An AI feature’s delay includes more than the model: authentication, retrieval, tools, network time and rendering can all contribute. Measure the stages before choosing a faster-looking model.
This lets you improve the wait without removing evidence or skipping validation that the task needs.
Measure the workflow
Separate waiting in your own queue, retrieving evidence, provider response and downstream tools. A slow file lookup will remain slow after a model change. Use enough comparable tasks to see whether the delay is consistent rather than one network outlier.
Reduce unnecessary work
Remove repeated context that adds no value, limit output to what the task needs and avoid serial calls that are genuinely independent. Preserve authorization and verification steps. Use caching only under the provider’s supported conditions and confirm actual hits.
Recheck the complete task
A shorter answer that omits the needed steps is not an improvement. Evaluate correctness, failures and total time alongside first-output latency. Record the configuration and measured sample; do not turn a theoretical vendor speed claim into your own benchmark.
Before you act
A faster first token is not necessarily a faster completed task. Different task lengths and network conditions make casual one-request comparisons unreliable.
What to do next
- Record timings for the main request stages without logging sensitive content.
- Repeat representative tasks under comparable conditions and identify the largest delay.
- Change one stage and compare completion time as well as answer quality and error rate.
