Craft
ChatGPT or a Dedicated PRD Tool? An Honest Answer
For many teams a general assistant with good context drafts a usable PRD. The specific point where a dedicated PRD tool earns its price, and where it does not.
We sell a dedicated PRD tool, so weigh this accordingly. The honest answer to the question in the title is less convenient for us than you might expect.
For a lot of teams, a general assistant with good context is enough. Any capable model will draft a well-structured PRD on request. Give it the real background, make it ask questions before it writes, review what comes back, and you will get a usable first draft without buying anything.
What a dedicated tool sells is not better sentences. It is the things around the draft that you would otherwise supply yourself, every time: the context, the method, and a review that takes a position. Whether that is worth paying for depends on how many of those you already supply well, and how many people need to supply them.
What a general assistant does well
Worth being specific, because the case for it is real.
Drafting from good input. Paste a clear problem statement, the constraints and the decisions already made, and a general assistant turns them into a structured document in a minute. The output tracks the input. That is true of every tool in this category, ours included.
Flexibility. It will rewrite a section for a different reader, summarise interview notes, argue the other side of a scope call, or list edge cases for a flow. A dedicated tool is built around one workflow. A general assistant is built around whatever you ask next.
No adoption cost. You probably already pay for one, you know how to use it, and nobody has to agree on a new tool. That is not a small advantage. A tool nobody opens enforces nothing.
If you are one PM writing a handful of specs a quarter, you know what a good one contains, and a colleague reads your drafts adversarially, you may not need anything else. That is a fine place to be.
Where it runs out
The limits are not about model quality. They are about what the assistant does not bring to the conversation unless you bring it.
Context has to be re-supplied. A general assistant works from what you put in front of it. It does not know the approach your team rejected last spring, or which constraint is a contract rather than a habit, unless someone writes that down and hands it over for every spec. Missing context is why AI-written specs come out fluent and generic.
The method lives in your prompt. It does what you ask. If you do not ask about the empty state, the permission rule or the baseline, it will not raise them. So the standard your specs meet is whatever you remembered to type that day, and prompts drift between specs and between people.
Review defaults to agreeable. Ask for a critique and you tend to get even-weighted suggestions with no view on which one blocks the build, which is the failure described in can AI review a PRD. A review is useful when it ranks. And a recent benchmark found single-run evaluations of requirements statistically unreliable: one pass is a sample, not a verdict.
The standard does not travel. One PM with a good prompt library is a person with a method. Five PMs with five prompt libraries are five standards, and the engineers reading their specs notice.
The point where a dedicated tool earns its price
Put the four limits together and the threshold is fairly specific. A dedicated tool is worth paying for when the standard has to hold across more than one person or more than one spec, and you would otherwise be holding it by hand.
| Your situation | General assistant | Dedicated tool |
|---|---|---|
| One PM, a few specs a quarter, a sharp reviewer nearby | Enough | Optional |
| You paste the same background into every conversation | Workable, with discipline | Earns its keep: context persists |
| Several PMs writing to one bar | Several prompt libraries, several standards | Earns its keep: one method |
| Specs go to engineers or coding agents with little senior review | Risky: nothing checks the draft | Earns its keep: a fixed review before build |
| You mostly need rewriting, summarising, brainstorming | Better fit | Overkill |
Notice what is not on the list: writing quality. From the same thin brief, both kinds of tool produce a fluent draft. The one that asks what is missing, marks what it assumed and has your team's past decisions to hand produces a more specific one. That behaviour is what you are paying for, so test for it with the seven checks for a PRD generator rather than taking anyone's word for it, ours included.
Getting the most out of a general assistant
If the table says a general assistant is enough, five habits close most of the gap.
- Front-load the context. The problem, the evidence you actually have, the constraints marked hard or soft, and the decisions already made with the options you rejected. Two paragraphs of real context beat a page of prompt instructions.
- Make it ask first. "Before drafting, list the questions whose answers would change this spec. Do not write until I answer." Count what it asks. Zero questions means a draft full of guesses.
- Make it mark assumptions. "Label anything I did not tell you as Assumption." An invented baseline in the same voice as your facts is worse than a blank one.
- Review in a fresh conversation. Paste the draft into a new session with an explicit rubric, such as the PRD review checklist, and ask for findings ranked blocker, major, minor.
- Keep the prompts somewhere shared. If more than one person writes specs, the prompt is your standard. Version it like one.
An illustration of why step 2 matters. Say the brief is "let admins export the member list." With no instruction to ask, a draft arrives with a row limit, a success target and a permission rule, all plausible and none decided by you. With the instruction, the same assistant comes back with questions about who counts as an admin and whether the file should contain email addresses. The second response is less impressive and far more useful.
Do all five and you have rebuilt most of a dedicated tool by hand. That is a legitimate choice. The question is whether you will keep doing it on the twentieth spec of the quarter, and whether everyone else on the team will too.
Where ours sits
Our generator is built around the parts a general assistant leaves to you. It runs in four steps (Coach, Context, Draft, Publish), scores your brief on six dimensions before writing anything, and reads your workspace Brain, meaning past docs, decisions and team standards, instead of starting from an empty prompt. The critique runs eleven named passes and lands each finding as a comment with a severity and a suggested rewrite.
That is the design, not a result, and it does not change the answer above. If a well-prompted assistant and a sharp colleague already give you specs your engineers can build from, keep doing that.
On price
We do not quote other tools' prices in posts, because they change and a stale number in a blog post is a small untruth. The comparison page lists the dedicated tools with dated prices. If you are weighing ChatPRD specifically, there is a head-to-head.
The comparison that matters is not one subscription against another. It is the tool's cost against the time you spend supplying context, method and review by hand, and against the cost of the spec that reaches a sprint with a gap nobody checked for.
FAQ
Is ChatGPT good enough to write a PRD? For drafting, often yes, if you supply the context and review the result.
When is a dedicated tool worth paying for? When the standard has to hold across several people or specs and you would otherwise hold it by hand.
ChatPRD or ChatGPT? Compare what each supplies that you would otherwise supply yourself. Dated prices are on the comparison page.
Can it review its own draft? Not in the same conversation. Use a fresh one with an explicit rubric and ranked severity.