Draft a Discovery-Session Recap From Your Notes With AI at the Cursor

You can speak your verified notes into a compatible focused Windows text field, ask AI for a structured recap, and review the draft before it goes anywhere. The recap works best when you keep four fields apart: the observed issue, the evidence, your hypothesis, and the proposed next step. Speak them in that order, with a clear instruction to flag gaps and invent nothing, and the first draft should keep what you saw separate from what you suspect, though you still need to check that it did. You still decide what is accurate, what is shareable, and what the client should read.

SpeakLingo supports this workflow through Syping, its approach to speaking and typing in one flow at the cursor. Below you will find an illustrative before-and-after example, a reusable template, a review checklist, and a method for measuring time to reviewed output using your own work rather than anyone else's numbers.

A discovery recap is a working document. The client uses it to confirm you understood them. Your team uses it to decide what to investigate next. Your future self uses it to remember why a hypothesis was ever on the table.

Recaps tend to go wrong in a predictable way. Take the sentence “Order handling seems fragmented and is probably causing delays.” It blends three different things: something observed (order handling uses several tools), something inferred (“fragmented”), and something unverified (“causing delays”). Once those blur together, the client may correct the wrong point, or a hypothesis may travel into a steering-committee deck as a finding.

A four-field structure guards against that:

• Observed issue: what you saw or were told, stated plainly.
• Evidence: the source for each observation, such as a named interviewee's statement, a document you were shown, or a process you watched. Say whether you verified it.
• Hypothesis: your current explanation, marked as untested.
• Proposed next step: the action that would confirm, refute, or refine the hypothesis, with an owner if one has been agreed.

A fifth block is worth adding: missing facts and open questions. The example prompt for this workflow asks the AI to flag missing facts, so gaps are surfaced in the draft instead of left for you to discover later.

The workflow draws on three SpeakLingo capabilities, described in more detail on the features page:

1. Online transcription in 99+ languages. Your speech becomes text in a supported language that suits you and your reader.
2. AI contextual formatting. Text can be arranged into a structure that suits the document, such as headings and bullets for a recap.
3. Spoken AI text requests at a compatible focused Windows text field. You give a task instruction aloud, and the resulting draft appears where your cursor is.

Compatibility is not universal. The flow works with text fields that support it, so try your preferred email composer, document editor, or notes application before relying on it. You can also type at any point: correcting a client's name or adding a line by hand is part of the same flow.

One boundary matters before you start. Dictating notes into a field is transcription, and it does not by itself turn on AI. To get a drafted recap, use the supported AI Task workflow and put the instruction and every fact you want used (the four fields, the evidence sources and the verification status) together in that one request. Earlier dictations, text already sitting in the field, and whatever 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. If a fact is not in the request, the AI cannot reliably include it correctly, so do not expect it in the draft. Check the draft for gaps or guessed details, and add the fact yourself during review. Transcription has its own setup and access requirements, so if AI is unavailable you cannot assume transcription is available either.

A practical sequence:

1. Set up for safe drafting. If your setup includes an optional Enter-after-delivery behavior, turn off automatic Enter or send behavior before you start. In some apps that setting could submit a message the moment text lands. For a recap you want the draft sitting in the field, unsent, until you have read it.
2. Gather your verified inputs. Open your session notes and pull out only what you can stand behind. Decide which items are observations and which are your interpretations.
3. Place the cursor in the document or draft field where the recap will live. A document or notes page is a safer first target than an open email to the client.
4. Speak the task instruction and the four fields. Name each field as you go, for example “Observed issue: …” and “Evidence: …”.
5. Include the guardrail. Ask for missing facts to be flagged and for nothing to be invented.
6. Read the draft against your notes, line by line, using the checklist below.
7. Edit, then decide. Send, share, or file the recap only after you are satisfied with it.

The potential benefit sits in step 4. If you would otherwise type notes in one window, copy them into a separate AI window, wait for a draft, and paste it back, the cursor-based flow may remove those extra hops where it fits your setup. Structuring still takes your judgment. What may change is the manual effort of arranging the first draft, and the measurement section below shows how to check that for yourself.

This workflow is about dictating your own notes after the session, or while reviewing them. It is not a way to record or transcribe the meeting itself. That keeps you in control of what enters the draft and ties the recap to material you have already verified.

The scenario below is fictional and illustrative. The company, people, and facts are invented to show the practice, and the draft is example output, not something observed in a real session or test.

Context: You are a consultant who ran a first discovery session with the operations lead at a fictional regional supplier, Harbor Lane Supplies. Your notes are shorthand, written in the order things came up.

Before: raw notes

HL ops call. Ops lead (Dana) says order status questions come in by phone, email, and shared inbox. Seems like nobody owns follow-up? Dana showed a spreadsheet where customer service logs callbacks, said it's usually up to date. Probably the root cause of late replies is tool sprawl. Dana wants a fix by quarter end. Should look at the inbox + spreadsheet. Ask about warehouse system, didn't get to it. Maybe a single queue would help??

Sent as-is, these notes would confuse a client. They mix reported statements, impressions, and a cause nobody has tested. The warehouse question is buried near the end.

The spoken instruction (at the cursor)

You speak, naming each field:

“Using only the inputs in this request, draft a discovery-session recap for Harbor Lane Supplies, operations workstream. Observed issue: order-status questions arrive by phone, email, and a shared inbox. Evidence: Dana, the operations lead, described these three channels, and showed me a spreadsheet where customer service logs callbacks. She described it as usually up to date; I have not verified that. Hypothesis, untested: multiple channels without a clear owner may be contributing to late replies. Proposed next step: review the shared inbox and the callback spreadsheet with Dana's agreement, and ask about the warehouse system, which we did not cover. Use a clear professional structure. Flag missing facts; do not invent them.”

After: the draft that appears at the cursor (illustrative, not observed output)

Discovery-session recap: Harbor Lane Supplies (operations)

Observed issue Order-status questions reach the business through three channels: phone, email, and a shared inbox.

Evidence - Dana (operations lead) described the three channels. - Dana showed a spreadsheet used by customer service to log callbacks. She described it as usually up to date; this has not been verified.

Hypothesis (untested) Having several channels without a clearly assigned owner may be contributing to late replies.

Proposed next step - With Dana's agreement, review the shared inbox and the callback spreadsheet. - Ask about the warehouse system, which was not covered in this session.

Missing facts and open questions - Who owns follow-up on order-status questions is not stated. - The extent and nature of late replies is not documented in the inputs. - The warehouse system's role is unknown.

What the consultant changes in review

The draft is a starting point, so you read it against your notes and edit:

• The missing-facts list says ownership of follow-up is not stated. Your notes only suggested nobody owns it, with a question mark, so the draft's wording is the safer one and you keep it.
• Dana wanted a fix by quarter end. You left that out of your spoken inputs. You decide it belongs under a separate heading on client expectations, and you add it by hand, attributed to Dana.
• “Late replies” rests on Dana's description, so you reword the hypothesis to say “reported late replies”.
• You check the spelling of the client name and of Dana's name, since transcription of proper names may be wrong.

The reviewed version is the one you would send. The first draft stays with you until you decide otherwise.

Keep two pieces handy: the spoken instruction and the recap skeleton.

Spoken instruction template

“Using only the inputs in this request, draft a discovery-session recap for [client name and workstream]. Observed issue: [what you saw or were told]. Evidence: [source, who said it or what was shown, and whether verified]. Hypothesis, untested: [your working explanation]. Proposed next step: [action, owner if agreed]. Use a clear professional structure. Flag missing facts; do not invent them.”

For several issues, say “Issue one,” “Issue two,” and so on, keeping each issue's four fields together. More workflow ideas for consulting and other roles are collected on the use cases page.

Recap skeleton

[Client / workstream]: discovery-session recap Session date and participants: [fill in]

Observed issue(s)

Evidence (source; verified or not)

Hypothesis (untested)

Proposed next step (owner, if agreed)

Missing facts and open questions

Items for client confirmation

The last block, “Items for client confirmation”, invites the client to correct anything you misheard or misunderstood. Add it to your spoken instruction if you want the draft to include it.

This table shows what each field should contain, how to phrase it aloud, and what to check afterward.

AI output needs review before you use it. For a discovery recap, these checks matter most:

• Evidence versus inference: every observation maps to something in your notes, and every inference sits under “Hypothesis”.
• Attribution: statements are credited to the right person and role, and one person's view has not been turned into the organization's position.
• Verification status: anything unverified is marked as such, and words like “confirmed” or “clearly” have not crept in.
• Invented detail: no figures, dates, system names, owners, or causes appear that you did not supply.
• Names and terms: client names, product names, and acronyms are spelled correctly.
• Scope of next steps: each step was actually proposed or agreed, and none has quietly become a commitment.
• Sensitivity: the recap contains nothing the client or an interviewee would not expect to see shared, and you have considered who will receive it.
• Tone and audience: the wording suits the client reader as well as your internal team.
• Hedging: hypotheses are hedged accurately, without becoming so vague that they say nothing.
• Final ownership: you would be comfortable defending every sentence if the client asked where it came from.

Your engagement terms, your firm's policies, and the client's expectations govern what you share. The checklist is an illustrative writing practice. It is not professional advice or a compliance procedure, and the judgment on every point stays with you.

Do not rely on anyone's claim about time saved, including ours. Measure your own process with one metric: time to reviewed output. That is the time from opening your notes (or from the end of the session, if you prefer a consistent start point) until you have a recap you are willing to send.

A simple method:

1. Pick a comparable baseline. Choose a few recent recaps of similar scope that you wrote your usual way. Note how long each took from opening your notes to ready-to-send, including edits.
2. Time the new workflow the same way. Start the clock when you open your notes and stop it when the reviewed version is ready. Include the speaking, the review against the checklist, and any rewrites.
3. Record the editing load separately. Note roughly how much of the draft you changed and why: factual corrections, attribution fixes, tone, or structure. Frequent factual corrections suggest your spoken inputs need to be cleaner.
4. Compare like with like. A two-issue recap and a ten-issue recap are not comparable, so group by size.
5. Repeat over several sessions. One recap tells you little. Patterns across several show whether the workflow suits you.

If reviewed output takes as long as before, the workflow is not helping you, and you can drop it without having relied on an unproven claim.

Language. Online transcription supports 99+ languages. Use a supported language that suits you and your reader. This is transcription, not translation, and it does not guarantee AI output in every language. Check that the draft reads correctly in the language you chose.

Connectivity and access. Spoken AI requests need an internet connection, an enabled service, your consent, and access. AI drafting does not work offline. Offline English transcription is only a fallback, and only where a local runtime has been configured. It does not provide AI requests, so you would structure the recap yourself. AI being unavailable also does not show that transcription is available, because transcription has its own mode, setup and access requirements.

What may be sent remotely. Audio and text may be sent to remote services to produce transcription and AI output. If you opt in to screen context, that context may also be sent. Before using the workflow with client material, consider what you are comfortable sending, what your engagement terms allow, and whether you need the client's permission. That decision is yours.

Compatibility. Test the workflow in the text field you plan to use. Not every field will work, so also check what any optional Enter-after-delivery setting does in that app, and keep automatic Enter or send turned off while you draft.