From transcript to product spec: a 20-minute workflow

Most product decisions get made in a meeting. The meeting has a discussion, the team agrees on a direction, somebody says "I'll write this up", and then for the next three days that person tries to find an hour to actually write the spec while everyone else moves on to the next thing.

We've all been the person trying to write up. We've all been on the receiving end of "I'll write this up" that takes a week. The gap between the decision and the spec is one of the most consistent failure modes in product work.

This is a workflow we use to close that gap. It's not magic. It still requires a human. But it consistently takes us 20 minutes of focused work to go from "we had the discussion" to "first-draft spec, ready for review."

The starting point: you had the meeting

Assume you've had a 45-60 minute meeting where a feature was discussed. The participants were a mix of PM, engineering, design, maybe a senior stakeholder. Decisions were made. Trade-offs were considered. There was some back-and-forth. The meeting ended with broad agreement on what to build.

You have the transcript. The transcript is, say, 12,000-15,000 words. A typical PRD or design doc is 1,000-2,000 words. So the work is compression and structuring, not generation.

This is the right framing. You're not writing a spec from scratch. You're extracting and structuring something that already exists.

Step 1: Extract decisions (3 minutes)

The first move is to extract the decisions from the transcript. Not the discussion. Not the deliberation. Just the moments where the conversation shifted from exploring to committing.

In our copilot, this is built-in. The decision extractor reads the transcript and produces a list of decision points with timestamps. The output looks like:

Decisions (5):

Build the new feature as an opt-in for Pro users only (00:08:32 — Alice)

Use the existing notification infrastructure rather than building a new pipeline (00:15:40 — Bob)

Ship to 10% of Pro users initially, expand if metrics are positive (00:28:15 — Carol)

Don't add a settings page yet — toggle is global for now (00:36:08 — Alice)

Launch target: end of Q3 (00:42:50 — Dave)

If your tool doesn't do this automatically, you can do it manually by skimming the transcript and grep'ing for moments where someone says "let's", "we'll", "we agreed", "decided". Either way, this step takes 3-5 minutes and produces the spec's skeleton.

The decisions list is the most important artifact in this workflow. Everything else hangs off it.

Step 2: Pull the supporting reasoning (5 minutes)

Each decision has reasoning behind it. The decision "use existing notification infrastructure" was probably made because somebody pointed out that building a new pipeline would push the launch to Q4. That reasoning belongs in the spec.

For each decision, find the discussion that led to it. In a transcript, this is typically the 60-120 seconds before the decision moment.

Examples from our hypothetical meeting:

Decision: Use existing notification infrastructure

Context: Bob said the existing system handles 95% of cases. Alice raised the concern about the rate-limit ceiling. Mike confirmed we're 40% below the ceiling so we have room.

Decision: Ship to 10% of Pro users initially

Context: Carol argued for staged rollout because we don't have great error monitoring on this code path. Alice agreed but pushed back on going slower than 10% — the feature won't get enough signal at 5%.

The "context" sentences are what turn a decision into a spec. Without them, the reader knows what was decided but not why. With them, the reader can evaluate whether the decision still makes sense as circumstances change.

This step is where the AI helps a lot. Ask it to pull the reasoning behind each decision; it does a reasonable job. Sometimes it includes too much; you'll trim. Sometimes it misses nuance; you'll add. But you're starting from a draft, not a blank page.

Step 3: Structure with AI assistance (10 minutes)

You now have decisions and reasoning. Time to put them into spec format.

A typical PRD or design doc has these sections:

Problem statement (why we're doing this)
Goals (what success looks like)
Non-goals (what we're explicitly not doing)
Approach (what we're building)
Trade-offs (what we considered and rejected)
Open questions (what we still don't know)
Launch plan (how and when we ship)

You don't have to write these from scratch. You have decisions; map them to sections.

In practice, we use an AI prompt like:

"Given the decisions and reasoning below, draft a product spec with sections [problem statement, goals, approach, trade-offs, open questions, launch plan]. Use the decisions as the source of truth. Flag anything that seems missing."

The AI produces a first draft. It's usually 80% right. The non-obvious thing is that the AI is unusually good at spotting what's missing — the section that doesn't have enough material from the discussion. Common gaps:

The problem statement is thin (the meeting jumped to solutions too fast)
The goals are unstated (the team knew them implicitly but didn't articulate them)
The "non-goals" section is missing entirely (this is the most common gap)
The open questions weren't called out (the team agreed on the main thing but left smaller things unresolved)

These gaps are good signal. They're the parts of the spec that you, the writer, need to fill in by either asking the meeting participants follow-up questions or making explicit calls.

Step 4: Edit for what matters (2-7 minutes)

The AI draft is 80% there. The remaining 20% requires a human who knows the domain.

Specifically, you need to:

Sharpen the framing. The AI's problem statement is often too literal — it describes what the meeting said, not the underlying business problem. Tighten it.
Cut what isn't load-bearing. Transcripts contain a lot of "we should probably" and "we'd want to" that doesn't survive translation to a spec. Cut it.
Add what's missing. Often the team agreed on something implicit that needs to be stated. If three people referenced "the obvious metric for this" without naming it, you need to name it.
Reconcile tensions. Sometimes the transcript contains conflicting opinions that the meeting didn't resolve. The spec has to either take a position or call out the unresolved question.
Add structure that wasn't in the discussion. The launch plan, in particular, often needs to be sketched in more detail than the meeting covered.

This is where domain knowledge matters. The AI can structure what's there. It can't reliably fill in what isn't.

Two to seven minutes of editing, depending on how much the AI got right.

The full workflow timing

Step 1: Extract decisions — 3 minutes
Step 2: Pull reasoning — 5 minutes
Step 3: Structure with AI — 10 minutes (most of which is reading and adjusting the AI output)
Step 4: Human edit — 2-7 minutes

Total: 20-25 minutes from transcript to first-draft spec.

Without the workflow, the same spec typically takes 2-4 hours of writing time, spread across 1-3 days. The compression isn't because the AI is doing the thinking; it's because the AI is doing the structural work, freeing the human to focus on judgment calls.

What this workflow doesn't do

A few honest caveats.

The first draft isn't shippable. It's a starting point for review. The team that was in the meeting will have edits. Reviewers will have questions. The spec needs at least one round of feedback before it's done. The workflow saves the writing time, not the iteration time.

Some meetings don't compress. If the meeting was a brainstorm with no real decisions, the workflow produces a thin spec that mostly reveals the meeting didn't actually conclude. This is useful signal — the meeting needs a follow-up — but the workflow can't manufacture decisions that weren't made.

The AI sometimes hallucinates. It can confidently summarize a decision that wasn't actually made, or attribute a quote to the wrong person. The human has to read carefully and check against the transcript. This is the one step you can't skip.

Domain language matters. If your meeting involves heavy use of internal jargon, the AI will sometimes miss the meaning. Engineering meetings are particularly prone to this. Reading the AI output critically is essential.

The compound benefit

The most interesting thing about this workflow is what happens over time. When the gap between decision and spec drops from "3-day delay" to "20 minutes after the meeting", a few things change in how the team operates:

Meetings produce specs more reliably. When the writing step is cheap, people do it. When it's expensive, they don't.
Specs get reviewed while the meeting is fresh. Reviewers remember the context. Their feedback is sharper. Spec quality goes up.
Decisions get questioned faster. If a decision was made on Monday and the spec is reviewed on Wednesday, anyone uncomfortable with the decision can push back while it's still cheap to change. With a 1-week gap, the decision often becomes load-bearing for other work before anyone has a chance to push back.
Documentation actually exists. This sounds obvious but is the most underrated outcome. Most product decisions in most companies are not well documented. The workflow makes documentation cheap enough that it actually happens.

A small political note

A side benefit nobody talks about: when the AI does the structuring, the spec is less "the writer's version of what happened" and more a structured surface of what was actually said. This reduces a category of soft conflict — "you wrote up the meeting unfairly" — that happens more than people admit.

The AI's draft is neutral, in the sense that it doesn't have a political stake in how the decision is framed. The human edit is where bias enters, but the starting point is less loaded. For controversial decisions, this matters.

The 20-minute promise

Twenty minutes is not an exaggeration. We've done this dozens of times. The first time you do it, it might take 40 minutes — you'll be checking the AI's work carefully and getting used to the workflow. By the third or fourth time, you'll be at 20 minutes consistently.

The thing this replaces — "I'll write this up", followed by 3 days of guilt and a half-finished doc — is one of the most consistent productivity losses in product work. Closing it makes a real difference, both for the work itself and for the team's sanity.

If you've ever opened a Google Doc, stared at it for ten minutes, and then closed it to "come back tomorrow", this workflow is for you. The doc tomorrow is no longer required. The doc is twenty minutes after the meeting.