The useful AI PRDA model can produce a plausible answer while the product still fails. The requirement must describe the decision, evidence and recovery around that answer.
An AI product requirements document starts with one workflow
Start with a person doing a job, not with a chatbot. Imagine an accounts team that receives supplier invoices by email. The useful scope is not "use AI to automate finance." It is one sequence: receive an invoice, extract named fields, flag missing evidence, let a reviewer correct the result, and send approved data to the accounting system.
That sequence exposes the real product. It names the user, trigger, input, decision, handoff and end state. It also reveals where deterministic software may be better than AI. File validation, permissions and approval rules should not become probabilistic just because a model is present.
GOV.UK guidance on learning user needs recommends grounding user stories in evidence and linking them back to the need they serve. Its service scoping guidance also warns against letting a technical choice dictate the shape of the service. An AI PRD should preserve that order: workflow first, technology second.
Turn the workflow into an observable contract
A requirement becomes useful when another person can observe whether it passed. Replace "the assistant should be accurate" with a concrete moment, expected behavior and evidence. The product team can then prototype the interface, engineering can design the system, and the buyer can accept or reject the result using the same contract.
For the invoice scenario, the system may extract an amount and supplier name only when both appear in the source file. If the tax number is absent, the interface should say it is absent rather than invent one. A reviewer must see the original evidence next to the extracted value and be able to correct it before export.
| Vague request | Requirement to write | Evidence at review |
|---|---|---|
| Understand invoices | Extract six named fields from supported files | Field-level result linked to source evidence |
| Be accurate | Meet the agreed threshold on representative cases | Versioned eval set with pass and failure labels |
| Ask a human when needed | Route defined conditions to a named reviewer | Visible queue, reason and recorded decision |
| Learn from feedback | Store approved corrections for analysis | Correction log with owner and retention rule |
Specify data before models
The PRD should list each input, who owns it, where it comes from, whether the product may process it, and how long it is retained. Add examples of ordinary cases, rare cases and data the system must reject. A model name cannot compensate for missing access, disputed rights or a source that changes without notice.
Consider a support assistant that drafts replies from product documentation. The document set needs a source of truth, an update owner and a way to show which passages supported the draft. If an archived policy conflicts with the current policy, the PRD must say which source wins and how the stale source is removed.
NIST's AI Risk Management Framework Core calls for intended purposes, deployment context, knowledge limits, third-party components and human oversight to be documented. For a buyer, those are not governance decorations. They change architecture, delivery time and what a team can safely promise.
Write evals beside the requirements
Variable output needs stable tests. Build a small, versioned evaluation set from real or safely representative cases before polishing the interface. Each case should include the input, expected qualities, unacceptable failures, grader and reason the case matters.
Suppose the product summarizes customer calls. A useful set includes a routine call, conflicting requests, an unclear commitment, sensitive information and a call with no action item. The grader can check whether the summary separates facts from suggestions, preserves names correctly and refuses to create a commitment that was never made.
OpenAI's current evaluation guidance describes evals as a way to test variable AI systems against explicit expectations. NIST similarly recommends documenting test sets, metrics and conditions similar to deployment. The PRD should therefore define the test asset, not merely say that evaluation will happen later.
Match human control to the consequence of error
Human review is not one universal checkbox. The PRD should state which actions the system may take, which it may only propose, and which require a named approval. A low-risk draft can be editable by default. A credit decision, medical instruction or irreversible account change needs a different control surface and may be unsuitable for autonomous execution.
In a hypothetical sales tool, AI might summarize a public company page and suggest a message. It should not invent a client fact or send the message without the permission defined by the product. The reviewer needs the source, the proposed change and a clear approve, edit or reject path.
Google PAIR's mental models guidance recommends communicating limits and providing a non-AI way to complete a task when the system fails. Turn that principle into screens and acceptance criteria: show uncertainty where it matters, explain the next action, and keep a safe path open.
Make operations part of product scope
The released product needs owners and signals. Record what is logged, how feedback is captured, which quality and latency measures are watched, who receives an incident, and what causes a rollback. Include model and prompt versions so a changed result can be traced to a changed system.
For example, an extraction feature may pass launch tests and then encounter a new supplier format. The useful operating view connects the failed file type, confidence signal, reviewer correction and deployed version. A dashboard that only shows token spend will not tell the team whether customers completed the task.
This is where an AI proof of concept and a production requirement separate. The proof of concept tests whether a workflow can create value. The PRD also defines how that workflow will be supported, changed and stopped when reality moves.
Bring a studio decisions, not a prompt collection
A studio can estimate the work when the brief contains the current workflow, users, sample inputs, system boundaries, integration owners, eval cases, approval rules and release constraints. Product, design, engineering, security and the domain expert should review the same document. If one group keeps a separate hidden definition of success, the estimate is fiction with nicer typography.
The first delivery can still be small. For an internal support workflow, a studio might prototype one intake path, one evidence view and one correction loop against twenty representative cases. The output is not a claim that the product is ready. It is a tested slice that reveals data gaps, interface needs and engineering risk before the scope expands.
Buyers comparing teams can use the PRD beside the guide on choosing an AI product development partner. Ask how the team will challenge the workflow, build evals, expose failure, protect data and operate the release. A portfolio image cannot answer those questions.
Use a lighter AI PRD when the risk is genuinely low
The serious objection is speed. A full document can become ceremony for a reversible internal experiment. The answer is not to omit requirements. It is to scale them to the consequence. A one-page brief may be enough when no sensitive data is used, every output is reviewed, no external action occurs and the experiment can be switched off immediately.
Do not use that exception to hide risk. A polished demo, a benchmark score or a long prompt is not a product requirement. None explains the user decision, production data, unacceptable failure or recovery path. Write those four things even for the smallest test.
The interface still matters. The companion guide to AI UX design covers visible states for progress, evidence, correction and recovery. The PRD decides when those states are required and who owns the decision behind them.
AI PRD checklist before you request a quote
If those answers fit on one page, they are enough to start a serious conversation. Send that page to Seuil Digital and ask for the smallest product slice that can test the workflow, the interface and the operating contract together.
- One priority workflow has a named user, trigger, end state and current baseline.
- Inputs, source rights, retention, integrations and owners are recorded.
- Representative cases include ordinary, edge and unacceptable outcomes.
- Each model-assisted step has observable acceptance criteria.
- Human approval and fallback paths match the consequence of error.
- Logs, feedback, release gates, rollback and live ownership are defined.
- The smallest useful slice and the decision after it are explicit.