BlogJuly 2026

Your PRD Is a Guess With Better Formatting

Most product teams have written the good one at some point. Edge cases handled, acceptance criteria numbered, non-functional requirements tucked into an appendix nobody quite gets to. It survives three rounds of review and reads like knowledge.

It is still a guess. A well-researched, nicely formatted guess, and that is not a criticism of the document. Every line in a PRD is an assumption about what someone will do, and at the moment of writing, most of those assumptions have not yet met a user.

None of which is an argument against writing PRDs. A PRD is a prior, the best available estimate before evidence arrives. The question is less whether to write one and more how much you are willing to build against it before finding out where it was wrong.

Anyone who has trained a model has seen this shape before.

Training starts with initialized weights. They are not correct, and nobody expects them to be. They are a starting position that lets the process begin. From there you run an epoch, measure loss, take a gradient step, and repeat. The model does not get good because the initialization was inspired. It gets good because the loop runs often enough to correct it.

A refined PRD is a carefully chosen initialization. Genuinely useful. Just not the thing that produces convergence.

Iterative development is the loop. Ship a thin slice, measure, adjust, ship again. Each release is an epoch. Each piece of observed behavior is a gradient signal. The roadmap gets updated.

Early on, though, there are not enough users to measure much of anything. Which is why design partners matter so much. A design partner is someone close enough to the problem to notice when your framing is off, and invested enough to keep telling you. In training terms, they are your first labeled data. Small in volume, hard to acquire, and disproportionately valuable, because a handful of high-context conversations will correct a bad prior that a thousand shallow signals never would.

Two or three engaged design partners tend to reshape a roadmap faster than a quarter of internal debate. They also validate something more fundamental than a feature list, which is whether the problem is worth solving at the price you intend to charge.

The caveat is that design partner feedback comes from a narrow distribution. It is high quality, and it is not yet representative. Fine-tuning on a small, excellent dataset works beautifully right up until you serve a population that looks nothing like it.

That points at the harder part of iterating. In machine learning, the loss function is handed to you. Someone defined the objective before training began. In product, you are searching for the loss function while you minimize it. Customers can describe what frustrates them. They cannot tell you what you are optimizing for. That stays a judgment call, and it tends to move.

So the real risk of iterating is not speed. It is descending steadily toward the wrong objective. Overfitting to the loudest voice in the room. Weighting the most recent conversation more heavily than it has earned. The gradient looks clean. The objective was wrong.

The guardrail is unglamorous but it holds. Try not to act on a batch of one. A single conversation is worth having, and it is not yet a pattern. Let signals accumulate until they survive more than one person's difficult week.

Both approaches can fail. A refined PRD fails expensively and late, because the news arrives at launch. Iteration fails quietly and repeatedly, settling into a local minimum, a product that works well for nobody in particular.

One asymmetry is worth keeping in mind. A model can restart training cheaply. Teams cannot, and every epoch carries a real cost in calendar, budget, and goodwill.

So write the PRD. Then hold it the way you hold initialized weights. Confidently, and loosely.