SideNote ProHow to get faster AI responses
Waiting on an assistant breaks the flow it was supposed to protect. Latency is not one thing, though, and knowing which part is slow tells you which fix will work.
What actually determines response time
| Factor | Effect | Your control |
|---|---|---|
| Model size and class | Often the largest single factor | High |
| Reasoning effort | Internal work before any visible output | High, where supported |
| Expected output length | Longer answers take longer, roughly linearly | High |
| Prompt size | More input to process before generating | High |
| Conversation context | Grows silently with every turn | High |
| Attached documents | Large extractions dominate a prompt | High |
| Folder excerpts | Up to ten passages added to the request | Medium |
| Context compaction | An extra provider call when a thread overflows | Medium |
| Provider latency and load | Queueing and time to first token | Low |
| Rate limiting | Retries with backoff | Low |
| Local model hardware | The whole story on a local provider | Medium |
The pattern is that most of the controllable latency is in what you send and which model you send it to, not in the network.
The three changes that matter most
1. Stop using a heavyweight model for light work
This is the single biggest one. Rewriting a paragraph on a large reasoning model is slow for no benefit at all: there is nothing to reason about. Keep a fast profile and use it for fluency tasks. See choosing the right AI model.
2. Lower reasoning effort on tasks with no hidden structure
Reasoning effort spends generated tokens before you see a word. On summarizing, rewriting or translating, that time buys you nothing. Provider default or a low setting is right there. See reasoning effort.
3. Ask for shorter answers
Underrated because it is so simple. *In three bullets* or *under 100 words* cuts generation time directly, and usually improves the answer, because a model told to be brief has to decide what matters.
Watch the context you are carrying
A conversation gets slower as it goes, and the reason is invisible: every turn re-sends the accumulated context. Turn thirty carries twenty-nine turns of history whether or not any of it is relevant.
- Start new threads.
Ctrl+Nis the most effective latency fix nobody uses. A new topic deserves a new thread. - Remove attachments you have finished with. A 40-page PDF stays in the context long after the question about it was answered.
- Lower the context budget if threads regularly run long. It forces earlier compaction and keeps requests smaller.
- Edit and resend instead of arguing. Three corrective turns cost three round trips and carry all three into every later request. Fixing the original question costs one.
Compaction itself is a provider call. When a long thread crosses the budget, SideNote Pro asks your model to summarize the older messages, which takes time. Frequent compaction usually means threads that should have been split.
If you run a local model
On a local provider, speed is hardware. Prompt processing and generation both run on your machine, so a long prompt costs time in a way it does not on a hosted endpoint with a large GPU behind it.
- Use a smaller model than you would hosted. The speed difference locally is much more dramatic.
- Lower the context budget. Local prompt processing is where long context hurts most.
- Keep attachments and folder retrieval for the questions that need them.
- Expect compaction to be noticeably slow, since it is a full model call on your own hardware.
Speed is not the goal
It is worth saying plainly: the objective is not to make every response fast. A hard debugging problem deserves a capable model and real reasoning, and thirty seconds of waiting for an answer that is actually right is an excellent trade.
The objective is to stop spending heavyweight inference on lightweight tasks. Most people's assistant use is dominated by small requests, and those are where the waiting is pure waste.