What is the difference between a PRD and a software requirements specification?

A PRD answers what and why: the problem, the users, the scope, and how success is measured, in business terms. A software requirements specification answers how the software must behave to satisfy that: the inputs, outputs, data, and rules in detail. The PRD is the brief everyone agrees to; the specification is the engineering detail that makes the brief buildable. On a small project the two often live in one document, but they answer different questions and it helps to keep them distinct.

How do you write a PRD for an AI build?

A useful small-project PRD covers six things and can be written in an afternoon: the real problem stated in business terms, the users and the job they are doing, what is in scope and what is explicitly out, success measured in numbers, the constraints including budget and data rules, and an AI-specific section. That last section names where the model fits, what it is allowed to decide, and where a person stays in the loop. How to write a PRD is mostly the discipline of answering those questions honestly before the work that depends on them begins.

Does AI mean you no longer need to plan software projects?

No. The opposite is closer to the truth. A METR randomised controlled trial found AI tools made 16 experienced developers 19% slower, even though they forecast a 24% speedup and still believed afterward they had been about 20% faster. A model is fast at producing code and unreliable at deciding what the code should be. Planning supplies the decision the model cannot make for itself, which is why an AI implementation plan and a clear specification matter more once code becomes cheap to generate.

How long should planning an AI build take for a small business?

Days, not months. For a small business, the planning phase runs from discovery through a written PRD and a sketched system to choosing the smallest version that proves the idea. The point is not to slow the project down but to remove rework, which is the genuinely slow part of any build. Set against a build that runs for weeks and a budget that cannot absorb a rebuild, a few days of planning is the cheapest insurance available.

Planning AI Builds: Why the PRD Comes Before the Prompt

Most AI builds fail in the planning, not the code. A product requirements document is how you decide what to build and why before a model writes a line of it. Plan first, specify the system, and the build phase becomes execution rather than guesswork. That sequence is the single biggest predictor of whether the result is worth the spend.

We have learned this the hard way and the cheap way. The cheap way is to write things down first. This article defines the PRD, corrects the belief that a better model can rescue a vague brief, sets out the six sections a small-project PRD needs, and walks through writing one this afternoon.

What a product requirements document is

A product requirements document, or PRD, is a written specification of what a product should do, who it is for, and how you will know it works, agreed before any building starts. It records the problem, the users, the features in scope, the features explicitly out of scope, and the measures of success. It is the contract between the idea and the build.

People ask what is a product requirements document because the term sounds heavier than it is. A PRD is not a hundred-page tome. For a small project it is a few pages that any non-technical owner can read and recognise as their own business. It sits alongside its more technical cousin, the software requirements specification, which describes the system's behaviour in detail: inputs, outputs, data, and the rules that connect them. The PRD answers what and why. The specification answers how the software must behave to satisfy it.

The reason this matters more with AI than it did before is a change in where the work happens. A large language model can produce working code in seconds. That speed is real, and it moves the bottleneck. The constraint is no longer typing the code. The constraint is knowing precisely what the code should do.

The evidence against build-and-see

There is a tempting story that AI removes the need for planning. The code is fast, so why not just build and see what happens. The evidence does not support the story.

In a randomised controlled trial run by METR, 16 experienced open-source developers completed 246 real tasks on their own mature repositories, mostly using Cursor Pro with Claude. Allowing AI tools made them 19% slower. The detail that should stop every founder is the perception gap. The same developers forecast a 24% speedup beforehand, and even after finishing, still believed AI had sped them up by about 20%. The tool feels fast while it is making you slower. That gap is corroborated in the peer-archived preprint by Becker, Rush, Barnes and Rein.

The lesson is not that AI is useless. We use these tools every day. The lesson is that undirected AI use does not reliably deliver the productivity people assume, and that you cannot judge a build by how fast it feels from inside it. Speed of typing was never the bottleneck on a project that mattered. Knowing what to type was. Planning is how you supply the decision the model cannot make for itself. The model is a builder you brief, not an architect you trust to design the system, and the brief is the PRD. Whether the finished build pays back is a question we take up in measuring AI ROI, and most of the answer is decided here, before the build starts.

A bigger model does not fix a vague spec

When an AI build produces the wrong thing, the common diagnosis is that the model was not good enough. The fix, on this view, is a newer model, a longer prompt, or another round of "make it better."

Here is why that fails. A vague brief does not contain enough information to determine the outcome. "Build us a booking system" is compatible with hundreds of different systems: with or without deposits, with or without staff calendars, with cancellation rules or none. A model given that sentence does not stop to ask which one you meant. It picks one, fluently, and the result looks finished whether or not it is right.

Thoughtworks named the remedy directly in late 2025. In a piece on spec-driven development, technology director Liu Shangqi argues that vibe coding is "too fast, spontaneous and haphazard," and that it produces "too much unmaintainable, defective, one-off code." His remedy is a clear specification written first, separating planning from implementation, and his finding is the one worth pinning to the wall: clear specifications, he writes, "can still help reduce model hallucinations." A model with a precise spec has less room to invent. A model without one fills the gaps with plausible guesses, and plausible is not the same as correct. Upgrading the model raises the quality of the guessing. Only the spec removes the guessing.

The same logic covers architecture. You decide the data model, the integrations, the API boundaries, and where any retrieval-augmented component gets its facts, before the model starts assembling parts. If the build involves agentic AI or tool use through the Model Context Protocol, you decide what those agents are permitted to do up front, on paper, where a permission is a sentence rather than an incident. The architecture is a planning decision. Handing it to the model to improvise is how you get a system nobody can maintain.

The six sections of a small-project PRD

People search for a product requirements document template hoping for a download that does the thinking for them. The template is the easy part. The thinking is the point. A useful small-project PRD covers six things, and it fits in a shared document a few pages long.

The problem. One paragraph on the real problem, stated in the language of the business, not the solution. "Enquiries take two days to answer and we lose a third of them" is a problem. "Build a chatbot" is a solution wearing a problem's clothes.
The users and the job. Who uses this, and what are they trying to get done. A feature with no named user is a feature nobody asked for.
Scope, in and out. The list of what is being built, and the equally important list of what is not. Naming what is out is how you stop a small project quietly becoming a large one.
Success measures. What changes, in numbers. "Cut response time from two days to two hours." A measure you cannot check is a wish.
Constraints. Budget, deadline, the systems it must connect to, the data it can and cannot touch. For an AI build this is also where privacy and access rules belong, a subject we treat on its own in AI security, privacy and trust.
The AI-specific section. Where the model fits, what it is allowed to decide, and where a person stays in the loop. This part is not optional, and we explain why in our piece on keeping a human in the loop.

To make the scope section concrete, here is one shaped like the ones we write, short enough to show whole:

In scope: enquiry form, instant acknowledgement email,
lead record created in the CRM, daily summary to the owner
Out of scope: quoting, invoicing, calendar booking, multi-language
Success: median first response under 2 hours within 6 weeks of launch

Four lines, and they have already made decisions a model would otherwise have made silently. That is the entire trick. How to write a PRD is mostly the discipline of answering the six questions honestly before the work that depends on them begins.

Write the PRD this afternoon: a walkthrough

You need a shared document, one other person who knows the business, and about three hours. The sequence matters less than the honesty, but this order works.

1.Write the problem paragraph without naming any technology. If a tool name creeps in ("we need a chatbot"), delete it and describe what is going wrong instead.
2.Name the users as real roles: "Sarah, who answers enquiries between site visits," not "users."
3.Draft the in-scope list. Then force at least three genuine candidates onto the out-of-scope list. If nothing painful is out of scope, the scoping has not happened yet.
4.Write success as one number you can check with the tools you already have: a response time, a conversion rate, hours saved a week. If you cannot measure it today, add "set up the measurement" to the scope.
5.List the constraints: the budget ceiling, the deadline, the systems the build must connect to, and the data it may and may not touch.
6.Write the AI paragraph: what the model does, what it may decide alone, and which of its outputs a person checks before a customer sees them.
7.Read the whole document aloud with the other person and mark every sentence where either of you hesitates or disagrees.

Expect steps 3 and 7 to be uncomfortable. That is the document working. Every disagreement surfaced at this stage is a defect caught in design, which is exactly where defects are cheapest.

Planning is the cheapest place to be wrong

The case for planning is, at bottom, a case about cost. A wrong assumption is cheap to fix while it is still a sentence in a document and expensive to fix once it is built into a system.

This is long-established engineering lore, often called the 1:10:100 rule: a defect caught in design is far cheaper to fix than one caught in implementation, which is far cheaper than one found after release. The best-known multipliers, summarised by Functionize, trace to internal IBM materials from the 1980s and should be read as directional rather than precise, since later work has found the escalation real but context-dependent. The direction is what matters: a wrong decision gets more expensive the longer it survives, and planning is the phase where it is cheapest to find.

Scope is the other half of the cost story. The Standish Group's CHAOS research, the long-running benchmark for IT project outcomes, has consistently found small projects succeed around 90% of the time while large projects succeed less than 10% of the time. Its original 1994 large-organisation data, only 9% succeeding outright with 61.5% challenged and 29.5% cancelled, set a pattern later reports kept confirming. Keeping a small project genuinely small is one of the strongest predictors of success, and a PRD that names what is out of scope is the tool that does it. AI sharpens this risk rather than removing it. When code is cheap to generate, scope expands quietly, one easy feature at a time, until the system is larger than anyone planned and harder than anyone wanted to maintain. The document is not bureaucracy. It is risk control with a word count.

Spec first or build first: the verdict

The fear is that planning makes a fast thing slow. Done well, it does the opposite, because the slow part of any build is rework, and rework is what planning removes. For a small business, the planning phase for an AI build runs days, not months. It starts with discovery, understanding the business and the real problem before proposing a solution, which we cover in the value of discovery and audit. From there you write the PRD, sketch the system, and pick the smallest version that proves the idea, a discipline we treat fully in taking an MVP from zero to one.

Planning is also where the build-versus-buy question gets decided, before money is committed. Some problems are solved by configuring software that already exists; some need a custom solution. A short planning phase is where you make that call deliberately rather than discovering, three weeks in, that you have rebuilt something you could have bought, a trade-off we examine in build versus buy.

So the verdict. Build first only when the project is a genuine throwaway: no customer data, no connection to your systems, and you are willing to delete it whole. Prototypes like that are useful, and the PRD you write afterwards is better for what they taught you. For anything that will touch customers, money, or data you cannot lose, spec first, every time. The developer's job has shifted toward exactly this specifying and directing work, a change we cover in the changing role of the developer.

The Enki Approach

We do not start AI builds in the code editor. We start in a document, with the problem, the scope, the success measures, and the system sketched out, because the build phase goes far better when the model is executing a decision rather than making one.

In our work building for small businesses, we treat the PRD and the architecture spec as the deliverables that protect everything downstream. We agree what is in and what is out, we write success as a number, and we decide where the AI fits and where a person stays in control before anyone runs a build. The model is fast, and we use that speed deliberately, pointed at a target we defined first. That is how planning an AI build pays for itself: not in the planning, but in the rebuild you never have to do.