This is the first in a series of articles documenting something I am still in the middle of. Not a finished product. Not a case study. An experiment in using AI to lower the barrier to one of the most important artifacts in product work and what I am learning along the way.
That artifact is the PRD.
A Product Requirements Document is not bureaucracy. It is clarity. It is the thing that forces a half-formed idea to answer hard questions before it consumes engineering hours. What problem are we solving? Who is it for? What does done look like? What are we explicitly not building?
When a PRD is done well, it makes the idea reviewable. Stakeholders can push back on the right things. Engineers can estimate accurately. Designers have a brief to react to instead of a vague verbal description.
The PRD is not the problem. The problem is what it takes to write a good one.
Writing a quality PRD requires fluency. You have to know what questions to ask, what to cut, what a realistic scope boundary looks like, and what success actually means in measurable terms. That fluency comes from experience. It is not evenly distributed across a product organization, and it is almost nonexistent outside of one.
So what happens? The PMs write them. Everyone else waits. Ideas pile up in backlogs that nobody trusts because half the items on the list were never properly defined. The people closest to the problem - operations, marketing, franchise partners, front line teams - have the observations and the instincts but not the tools or the training to turn them into structured artifacts.
That is the real cost. Not the time it takes a PM to write a PRD. The ideas that never get written at all because the right person was not in the room.
I built a workflow runner. One place where a product idea enters and a set of quality artifacts comes out — stakeholder interview, PRD, prioritization score, prototype. Chained together, not scattered across a dozen conversations and documents.
The goal is not to replace the PRD. It is to make producing one accessible without requiring years of product experience to do it well. Can the tool ask the right questions so the person just has to bring the context? Can it hold the structure so the output is usable without a senior PM cleaning it up afterward?
That is what I am testing. And I am learning as much about where AI gets stuck as I am about where it actually delivers.
More on both in the articles ahead.