This is a checklist you can actually work through before shipping or while debugging a slow AI feature. Each item has a short justification, because a checklist you do not understand is one you will skip. Treat it as a sequence of yes/no questions: if you cannot honestly answer yes, you have found something to fix.
It is organized into six groups — measurement, model, context, caching, serving, and perceived speed — that roughly mirror the order you should address them. Measurement comes first because everything downstream depends on having real numbers. Perceived speed comes last because it polishes an already-acceptable system.
Print it, paste it into a ticket, or fold it into your launch review. The goal is a working tool, not reading material.
Measurement
You cannot optimize what you do not measure. Start here, always.
- [ ] Latency split into segments — network, queue, TTFT, inter-token, total are logged separately, so you can find the dominant cost.
- [ ] Percentiles reported — p50, p95, p99 are tracked, because averages hide the tail that drives churn.
- [ ] Tested under realistic load — benchmarks run at expected concurrency, since latency behaves differently than at a single request.
- [ ] Token counts logged — input and output token counts per request, because they explain most latency variation.
Model
The model sets the floor on your latency. Choose it deliberately.
- [ ] Right-sized model — the smallest model meeting your quality bar is chosen, since unused capability is latency you pay for.
- [ ] Quantization considered — 8-bit or 4-bit evaluated when decode is the bottleneck, because decode is memory-bound and benefits directly.
- [ ] Quality verified after changes — any smaller or quantized model is scored on real tasks, so you do not trade speed for broken output.
Context
Context length is a silent latency tax. Keep it on a budget.
Items
- [ ] Context trimmed — only relevant tokens are sent, because every extra input token raises prefill cost and TTFT.
- [ ] History capped or summarized — old conversation turns are bounded, so context does not grow unboundedly over a session.
- [ ] Retrieval tightened — fewer, higher-relevance documents are retrieved rather than dumping everything in.
The mechanics behind why this matters are detailed in The Complete Guide to AI Inference and Latency.
Caching
Caching is the highest-leverage win most teams underuse.
- [ ] Prompt prefix cached — fixed system prompts are not reprocessed every call, removing repeated prefill.
- [ ] Full responses cached — repeated or near-identical queries return instantly instead of hitting the model.
- [ ] Cache hit rate measured — you know your hit rate, because a low one usually means an overly strict cache key.
Serving
How you serve requests determines behavior under load.
- [ ] Continuous batching enabled — new requests join in-flight batches, improving throughput and latency together.
- [ ] Batch window tuned to workload — tight for interactive paths, wide for batch jobs, matching whether a human waits.
- [ ] App co-located with inference — same region or network, to remove avoidable round-trip latency.
- [ ] Connections pre-warmed — cold-start spikes on first requests are avoided.
Perceived Speed
Once real latency is acceptable, make it feel even faster.
- [ ] Interactive responses stream — tokens appear as generated, so perceived latency tracks TTFT, not total time.
- [ ] Instant feedback shown — a typing indicator appears within ~100 ms so nothing feels frozen.
- [ ] Outputs capped to need — max-token limits match the actual use case, since shorter answers finish sooner.
These finishing moves are the same ones credited in Case Study: AI Inference and Latency in Practice for transforming a frozen-feeling assistant.
How to Use This Checklist in a Launch Review
A checklist only earns its place if it changes decisions. Fold it into the review you already run before shipping, and treat unchecked items as explicit risks someone has to own.
Turn it into questions, not boxes
A box invites a reflexive tick. A question forces a real answer. Instead of "percentiles reported," ask "what is our p99 TTFT under peak load, and where is it logged?" If nobody can answer on the spot, the item is not done, regardless of what the box says.
Assign each group an owner. Measurement and serving usually belong to whoever runs the infrastructure; context and caching often sit with whoever owns the prompt and retrieval logic. Diffusing ownership across the team is how items quietly go unchecked.
Decide which items block launch
Not every item is a launch blocker, but some are. We treat the measurement group as a hard gate: shipping a feature you cannot observe is shipping a feature you cannot debug. The rest become prioritized work, ranked by what your numbers say is the dominant cost. A skipped item with a written reason is acceptable; a skipped item nobody noticed is a future incident.
Adapting the Checklist Over Time
The checklist is not static. Traffic grows, models change, and a configuration that passed at launch can fail at scale. Build two recurring touchpoints so the list keeps working:
- On every traffic milestone — re-run the measurement and serving sections, because batching and queueing behavior shifts with concurrency.
- On every model or prompt change — re-verify the model and context sections, since a new model resets your quality and latency assumptions.
The items that age fastest are serving-related. A batch configuration tuned for ten concurrent users can buckle at a hundred, and the only signal is a creeping p99. Treat the checklist as living maintenance, not a one-time gate you clear and forget.
Frequently Asked Questions
In what order should I work through this checklist?
Top to bottom. Measurement first, because every later decision depends on knowing where time goes. Then model, context, caching, and serving in roughly that order, finishing with perceived-speed polish. The grouping reflects both dependency and leverage.
Which items give the biggest wins?
Caching the prompt prefix, right-sizing the model, and trimming context tend to deliver the largest gains for the least effort. If you are short on time, confirm those three first. They cover the majority of real-world latency problems.
Do I need every item before launching?
No, but you need the measurement group, or you are flying blind. The rest you prioritize by what your diagnosis reveals. A checklist item you can skip with a clear reason is fine; one you skip because you never measured is a risk.
How often should I re-run this?
Re-run the measurement and serving sections whenever traffic patterns change or you add load. Latency that was fine at one scale can degrade as concurrency grows. Treat the checklist as recurring maintenance, not a one-time gate.
What if I use a hosted API?
Many items still apply: measurement, context trimming, caching, streaming, region selection, and output limits are all in your control. Model right-sizing becomes choosing the right tier in the provider's lineup. Serving internals you cannot tune, but the rest you can.
Key Takeaways
- Start with measurement: segment latency, report percentiles, test under load, log token counts.
- Right-size and consider quantizing the model, verifying quality after any change.
- Treat context as a budget — trim, cap history, and tighten retrieval.
- Cache prompt prefixes and full responses, and watch your hit rate.
- Tune serving with continuous batching, co-location, and pre-warmed connections.
- Finish with streaming, instant feedback, and output caps for perceived speed.