Do you ever look at a task and wonder whether it would be faster to hand it to an AI or to simply do it yourself? Notestream is built around that exact decision: capture the task once, and then either finish it yourself or dispatch it to the right AI from the same queue.
Most advice about working with AI skips this step. It tells you how to write better prompts, but the earlier question matters more: should this task go to an AI at all? Get that call right and every tool in your stack gets more useful. This post gives you a four-question framework for making it, and shows where that framework lives in a real workflow.
The routing skill nobody teaches
The AI tools available to you are varied and capable. Claude tends to shine at careful writing and nuanced reasoning. ChatGPT is quick and versatile across everyday requests. Gemini handles long documents and research passes with ease. Builders on top of them specialize further: Claude Code writes working software inside your repository, and Lovable turns a plain description into a working design.
With that much capability on tap, the tempting answer is to hand everything off. But you have probably felt the other side of it: the task you spent twenty minutes explaining that you could have finished in ten, or the delegated draft that came back polished and wrong.
The skill that pays off is routing. Good routing means knowing which work to hand off, which to keep, and making that call quickly instead of relitigating it every time. Developers who work with AI all day make this call dozens of times before lunch, and the good ones make it in seconds because they use consistent criteria.
You can borrow those criteria. Here they are as four questions.
The four questions
Run any task through these in order. The answers point one direction or the other, and the whole pass takes under thirty seconds.
1. Can you write the brief in two sentences?
A task you can specify cleanly is a task you can dispatch. "Rewrite the pricing page intro to lead with the annual plan" hands off well. "Figure out what feels off about the pricing page" does not, because the work is in your head, not in the instructions.
If the brief would take longer to write than the task takes to do, keep it. If you find yourself writing a clean two-sentence brief without effort, that task wants to be dispatched.
2. Does it depend on judgment only you hold?
Some tasks rest on context no AI has: your read on a specific client, your taste in your own brand voice, a pricing call that commits your business for a year. Keep those, or do the deciding yourself and dispatch the producing. You make the call on the discount; Claude drafts the email announcing it.
The dividing line is decision versus execution. Decisions built on private context stay with you. Execution against a clear decision travels well.
3. Is reviewing cheaper than doing?
This is the question most people skip, and it does the most work. Every dispatched task comes back needing review, so the handoff only pays when checking the output is faster than producing it.
Research summaries pass this test easily: reading a summary beats compiling one. So does boilerplate code with tests attached. A legal-sounding paragraph you lack the expertise to verify fails the test, because the review is where the real work lives, and you cannot do it.
4. Will this task come back?
A one-off oddity can go either way. A recurring task should almost always be dispatched, because the brief you write today gets reused and refined every cycle. The weekly metrics summary, the monthly invoice chase, the research pass you run on every new prospect: write the brief once, dispatch it every time, and improve it as you learn what comes back.
Two or more dispatch answers and no hard keep on question two? Hand it off. Otherwise do it yourself, with no guilt either way. Doing a task yourself on purpose is a routing decision too.
Where the framework lives: capture first, decide later
A framework only helps if it meets your tasks somewhere. That somewhere should not be your memory, and it should not be six different chat windows.
This is the gap Notestream fills. It is an AI notes app and AI task manager built as a central hub for your AI workflow, and it separates the moments cleanly: capture now, classify on arrival, decide and dispatch when you are ready.
Capture is per-thought. You submit ideas, commitments, and deadlines as they occur, each one a sentence or two rather than a meeting-sized document. "Chase the two overdue invoices Friday." "Idea: record a setup walkthrough for new customers." "Rewrite the pricing page intro." Each capture takes seconds, so nothing waits on your willingness to organize.
Captures arrive through more than one door. The AI assistant field in the web app is the everyday surface: type the thought, submit, done. Forwarded email covers the work that starts in your inbox; send a client request to your capture address and it joins the same queue. And the MCP API lets the AI tools you already use file captures for you when an idea surfaces mid-conversation.
The moment a capture is submitted, Notestream's AI reads it and classifies it as a task, a reference note, a sub-task, or a reminder. That happens automatically, per capture, at submit time. There is no sorting session waiting for you at the end of the week, and you never choose a model for this step. Where you choose is the next move: which AI, if any, completes each task. Sorting and doing are separate jobs.
So the framework stops being a mental exercise and becomes a glance at a queue. Every task is already captured, already classified, already sitting in one place. You run the four questions on each item and either do it or send it.
Assign tasks to AI, one decision at a time
Here is the payoff on a real Monday. Your queue holds five tasks captured over the weekend, and you run the framework down the list.
The pricing page rewrite has a clean two-sentence brief, so it goes to Claude, which will hold your tone. The competitor research pass is cheap to review, so it goes to the in-app chat, where you can work with Claude, ChatGPT, or Gemini without leaving Notestream, and a long reading job suits Gemini well. The signup form fix is specified down to the field name, so it goes to Claude Code with the captured task as its brief. The onboarding page mockup goes to Lovable, which returns a working design rather than a description of one. And the decision about whether to raise prices stays with you, because question two said so.
Four dispatched, one kept, and the whole picture still visible in one queue: what is out, what is waiting, what is done. No pasted context, no hunting through chat histories.
That is the quiet advantage of a decision framework with a home. The thinking gets faster because the criteria are stable, and the doing gets faster because dispatch is a click rather than a copy-paste ritual.
Start with one week of real work. Capture every idea, commitment, and deadline in Notestream as it occurs, through the assistant field, a forwarded email, or the MCP API. Let classification sort the queue as it builds, then run the four questions on each task and route it honestly.
Try it free at notestream.ai.