Draft a Customer-Question Article Outline with AI at the Cursor

A content marketer can turn a customer question into a reviewable article outline by using the supported AI Task workflow and speaking one request that contains both the instruction and four verified inputs (the reader question, the direct answer, one example, and any source still needed), asking SpeakLingo to structure them at the cursor. The request tells the assistant to flag missing facts rather than invent them. You then check every claim, resolve each flag, and decide whether the outline is ready. Below are the workflow, a fictional worked example, a reusable template, a review checklist, and a method for measuring time to reviewed output with your own data.

An outline is an inexpensive place to correct a weak article. A vague question, an answer buried under background, or an example chosen because it came to mind first are all easier to repair as headings than as finished paragraphs.

Your expertise is usually already in your head or in a call note: what the customer asked, what the right answer is, and which case illustrates it. The structuring step is the part worth handing to a drafting assistant. That means ordering points, writing headings, and marking what still needs proof. You stay responsible for the facts.

Four inputs are enough to start:

• Reader question: the question in the customer's words.
• Direct answer: your answer in one or two sentences, which you know to be correct.
• Example: a concrete case, scenario, or walkthrough you can stand behind.
• Source needed: every claim that requires documentation, a subject-matter expert, or a policy page before publication.

The fourth input matters most. It tells the assistant where to stop and flag a gap.

SpeakLingo is built around Syping, the combination of speaking and typing. You can read the Syping explanation for the idea in full. In short, you speak into the place where you would otherwise type. For this task the sequence is:

1. Prepare the field. Open your outline document, brief, or CMS draft and click into the text field where the outline should appear. SpeakLingo is designed for compatible focused Windows text fields, not every application, so test your editor with a dummy outline before relying on it for a deadline. In the settings, turn off any automatic Enter or send behavior. An optional Enter-after-delivery setting can submit text in some apps, and you want the draft to stay put until you have read it.
2. Gather your inputs. Have the reader question, direct answer, example, and sources needed ready to say in your AI request. Online transcription supports 99+ languages, so you can use a supported language suited to your reader and team.
3. Speak the AI request. Use the supported AI Task workflow and speak the instruction and your four inputs together in one request, following the template below. The request asks for headings, bullets, and flagged gaps at your cursor.
4. Review in place. Because the draft lands in your document, you can edit it there. Where the cursor workflow fits your setup, this can reduce copying text between a separate AI window and your editor.
5. Send or publish only after your own check. Read the whole draft first. Then send it, share it, or hand it to a writer yourself.

One boundary matters here. Dictating alone does not enable AI. Use the supported AI Task workflow, and put the instruction and every fact it needs in that same request. Your earlier dictations, text already in the field, and what is on your screen are not automatically available to the AI. Screen context is a separate opt-in and may be sent to remote services, so leave it off unless you deliberately need it. Say your four inputs inside the request instead of assuming they carry over.

The AI steps have conditions. A spoken AI request needs an internet connection, an enabled service, your consent, and access. Audio and text, and screen context if you opt in, may be sent to remote services to produce the result. Let that shape what you dictate. Leave out unreleased commercial details, customer names, and anything your organization would not paste into an online tool.

For more on the product, see the features and use cases pages.

The scenario below is fictional. "Brightleaf Scheduling" is an invented appointment-scheduling product, and the facts are made up to show the shape of the workflow. They are not real product claims. The output shown is illustrative, not observed from a test of the app.

Before: scattered spoken notes

Imagine the marketer speaks these notes and receives plain transcription with no structure applied:

Customer asked can clients reschedule their own appointments without me getting double booked. Answer is yes if you turn on the calendar connection and set buffer time, clients only see open slots. Example, a small yoga studio owner who has two instructors and one room. Need to check whether the connection updates instantly or on a delay, and whether buffer time applies to every service or one at a time. Also somebody should confirm if there's a limit on how late clients can reschedule. I don't want to say anything about cancellation fees because that's not decided.

Everything useful is present. The order is conversational, though, and the open questions are mixed in with the answer. There is nothing yet to hand to a writer or a subject-matter expert.

The spoken request

The marketer then makes one AI Task request that carries both the instruction and the facts from the notes above, because the earlier plain transcription is not automatically available to the AI. The request is: "Using only these verified inputs, draft a customer-question article outline. Use a clear professional structure. Flag missing facts; do not invent them." The marketer then speaks the reader question, direct answer, studio example, and open questions in that same request.

After: a draft outline to review

An output of this kind might look like the following. It is illustrative, and real results vary and always need review.

Working title: Can clients reschedule themselves without causing double bookings?

Reader question: Can my clients reschedule their own appointments without my calendar getting double booked?

Direct answer (to confirm): Yes, when the calendar connection is turned on and buffer time is set, clients see only open slots.

1. The short answer - State the direct answer in two sentences. - Source needed: confirm the exact setting names in the product.

2. Why double bookings happen - Describe the problem from the owner's point of view. - Missing fact: no input supplied on common causes. Ask support or the product team.

3. The two settings that prevent them - Calendar connection: what it does. - Buffer time: what it does. - Source needed: does the connection update instantly or after a delay? - Source needed: does buffer time apply to all services or one at a time?

4. Example: a small studio with two instructors and one room - Walk through one reschedule from the client's side. - Missing fact: the sequence of screens. Capture it from the product before writing.

5. Limits to state plainly - Source needed: any cutoff for how late a client can reschedule. - Cancellation fees: leave out until a decision is made. Do not imply a policy.

6. Next step for the reader - Add the call to action approved by the team.

The gain here is structure and a clear separation between confirmed material and open questions. The outline adds no claim the marketer did not supply, and the undecided cancellation-fee topic is excluded. The marketer's next job is to resolve each flagged item with the product team or documentation, then revise.

Keep this in a note and speak it, or adapt it to your own wording. Square brackets hold your inputs.

Spoken inputs

• Reader question: [the question in the customer's words]
• Direct answer: [one or two sentences you have verified]
• Example: [a concrete scenario you can support]
• Source needed: [claims that need documentation, an expert, or a policy owner]
• Audience and tone: [who reads this and how technical to be]
• Out of scope: [topics to leave out]

Spoken request

"Using only these verified inputs, draft a customer-question article outline. Inputs: [reader question, direct answer, example, source needed]. Use a clear professional structure. Flag missing facts; do not invent them."

Optional additions

• "Put the direct answer first."
• "List each source needed under the heading it affects."
• "Suggest an FAQ section based only on the inputs, and mark any suggested question that needs a verified answer."

An out-of-scope list keeps the outline from drifting into topics you have not cleared, as the cancellation-fee example shows.

This table maps each outline element to what you speak, what the request should produce, and what you check.

Online transcription supports 99+ languages. If a reader's question arrives in a language you work in, you can speak your inputs in that language where it is supported. This describes transcription only. It is not translation, and it carries no guarantee that AI-written output will be equally strong in every language. If you publish in more than one language, ask a fluent reviewer to read each version. They should check that headings, product terms, and examples read naturally for that audience.

Run this before the outline becomes a brief or a draft. The checklist is an illustrative writing practice, not a standard or professional advice, and every item is your decision.

The question - Does the title reflect how customers actually phrase the question? - Is this one question, or two that should be separate articles?

The answer - Is the direct answer correct, complete, and placed where a skimming reader will find it? - Would a product or subject-matter expert sign off on it as written?

Facts and sources - Is every "source needed" flag resolved or assigned to a named person? - Did the outline add any claim, number, feature, or customer detail you did not supply? Remove it or verify it. - Are comparative or superlative statements supportable, or should they come out?

The example - Is it real and permitted for use, or clearly labeled illustrative? - Does it contain customer-identifying or confidential detail?

Scope and promises - Does the outline avoid guarantees about search ranking, traffic, or conversions? - Have the owners of any pricing, policy, legal, or compliance statements confirmed them?

Reader value - After reading the finished article, could the reader act on the answer? - Is there a clear next step that your team has approved?

Data handling - Did your dictated inputs avoid anything your organization would not send to an online service? - If you used screen context, did you opt in deliberately, and was the screen free of material that should stay private?

Text appearing in your document is a poor stopping point for a time measurement, because the outline still needs checking. A more useful measure is time to reviewed output: the span from starting to gather inputs for one outline to the point where you would hand it to a writer or editor with its flags resolved or assigned.

To measure it yourself:

1. Define start and end the same way every time. Start when you open the customer question. End when the outline has passed your checklist.
2. Include the whole path. Count input gathering, speaking, any re-asking, editing, and source-chasing. Log waiting time for other people separately so it does not distort the comparison.
3. Log several outlines under each method. Compare your usual process with the cursor workflow across a handful of similar questions rather than one.
4. Note difficulty. A question with settled facts and one with many open sources are not comparable, so tag each outline by how many source flags it carried.
5. Record rework. Note how many headings you rewrote and how many claims you removed. An outline that appears quickly but needs heavy correction may not shorten the total.
6. Compare typical values. The median across your log is more representative than a single best or worst run.

Whatever you find belongs to your team, your content, and your editor. This article offers no general figure.

• Compatibility. The workflow targets compatible focused Windows text fields. Test your editor, CMS, or note tool first.
• Online AI. AI requests need internet, an enabled service, your consent, and access. Audio and text, and optional opt-in screen context, may be sent remotely. Transcription has its own mode, setup, and access requirements, so an AI request being unavailable does not show that transcription is available.
• Offline fallback. Offline English transcription is only a fallback, and only where it has been configured beforehand, so do not assume it is ready. AI requests do not work offline, so make the request once you are back online, with the facts included in it.
• Review required. AI output can be incomplete, misordered, or wrong. The outline is a draft, and responsibility for accuracy stays with you.