A workshop transcript can become a polished product requirements document while gaps in the team's knowledge remain unresolved. A request, an interpretation and an approved requirement can look equally authoritative once they appear under tidy headings.

AI can help organise the material, identify gaps and draft clearer statements. The product team still needs a way to trace each important claim to its source and distinguish a proposal from a decision. This guide develops that approach through a hypothetical B2B customer-onboarding example.

Preserve the source before asking for a summary

Begin with an identifiable set of inputs: session notes, recordings where permitted, support cases, research findings and existing product decisions. Record the source, date, context and version. A workshop with internal stakeholders and a usability session with customers provide different kinds of evidence.

The GOV.UK guidance on taking research notes recommends recording observations, keeping each note focused and identifying its participant and session. It also addresses consent and the handling of personal information. For an AI-assisted process, agree which material may be processed and who may access it before uploading the source pack.

Keep a route back to the original passage. If notes are paraphrases, label them as paraphrases. If a transcript contains an uncertain speaker or unclear phrase, preserve that uncertainty rather than letting a summary quietly resolve it.

Give different statements different statuses

The GOV.UK research-analysis guidance separates observations from findings and subsequent actions. A useful extension for a PRD workflow is to distinguish five types of statement:

  • Evidence: a recorded observation, reported experience or available measurement, with its source and limits.
  • Inference: an interpretation of what the evidence may mean.
  • Hypothesis: an explanation or proposed relationship that can be investigated.
  • Proposed requirement: behaviour or a constraint the team is considering.
  • Accepted decision: a documented choice made by someone with the relevant authority.

These are proposed working labels, not an industry classification standard. Their purpose is to prevent a change in wording from silently changing a statement's status. An accepted product decision also does not prove that the hypothesis behind it is true; the team may deliberately choose a limited experiment.

Work through a small onboarding example

Imagine a B2B application in which company administrators invite colleagues. In a fictional stakeholder workshop, a support lead says: “Administrators ask us which invitations have expired.” A careful analysis could produce:

  • Source S-04: the support lead's statement, with its location in the session record. It establishes that the lead reported this issue; frequency and affected customer groups remain unverified.
  • Inference I-02: some administrators may lack a clear view of invitation status.
  • Hypothesis H-01: clearer status information could reduce avoidable support requests about invitations.
  • Proposed requirement R-03: an authorised organisation administrator can see the current status of invitations belonging to that organisation.
  • Validation questions: what do administrators currently see, which statuses confuse them, and how often does this issue occur?

The next investigation could combine observation of an administrator's task with a review of relevant support cases and confirmation of expiry rules with engineering. That may reveal a missing status, confusing wording or a different cause entirely.

The source statement does not establish demand for automatic reminders, bulk resending or a particular notification channel. Those ideas can remain visible proposals. They should not enter the accepted scope merely because a model can describe them convincingly.

Keep gaps and disagreements visible

Stakeholders may describe the same process differently. Sales might request shorter onboarding, support might request clearer status information, and an administrator might need to understand why an invitation failed. Preserve each perspective with its context before combining them into one problem statement.

GOV.UK's guidance on user needs emphasises research evidence and the user's underlying problem. Applied here, a stakeholder's preferred feature is a starting point for investigation, rather than sufficient evidence that the feature solves the user's problem.

For the example, maintain an open-question record containing the question, why it matters, the evidence needed, an owner and the decision it blocks. Give assumptions the same treatment. A missing baseline should appear as missing; the document should not invent a plausible support-volume estimate to complete a template.

When accounts conflict, ask whether they describe different roles, customer groups or stages of the process. If the conflict remains unresolved, show it to the reviewer instead of selecting the most confident-sounding statement.

Give AI a bounded transformation task

A practical first task is to extract and label statements, attach source references, list contradictions and draft questions. A later step can assemble a PRD from the reviewed material. Whether this uses a predefined workflow or a more autonomous agent is a separate design choice.

Specify that missing evidence remains explicit, quotations must match the source, proposals must retain their status, and unsupported quantitative claims must be omitted or flagged. Permit an incomplete answer when the source pack cannot support a complete section.

Structured output can make this material easier to process, but format does not establish correctness. OpenAI's Structured Outputs documentation explicitly notes that outputs can still contain mistakes and that unsuitable input can lead to hallucinations while the model tries to satisfy a schema. A required business field therefore needs a meaningful way to represent “not established.”

Check what each citation actually supports

A useful source reference lets the reviewer inspect a particular passage. For example, Anthropic's citations documentation describes location-based references and valid pointers to supplied documents. That helps with retrieval of supporting material.

The PRD still needs a separate judgment about meaning. A citation to one support lead's statement does not establish that most customers experience the problem. A quotation can be accurate while the surrounding conclusion overstates it.

For each important claim, ask whether the passage supports the stated actor, scope, timing and degree of certainty. Where several sources support an interpretation, retain those references together and explain the reasoning. Where evidence is indirect or contradictory, make that qualification visible in the document.

Build a PRD that preserves the evidence trail

For the onboarding example, the following compact structure would give reviewers a useful decision document:

  • Problem and affected users: describe the task and the difficulty, with supporting sources and remaining uncertainty.
  • Intended outcome: define the change the team wants to observe, the measurement approach and any baseline still needed.
  • Scope and exclusions: identify the behaviour under consideration and adjacent ideas deferred from this decision.
  • Proposed requirements: give each item an identifier, rationale, source links and review status.
  • Constraints and dependencies: record confirmed limits separately from assumptions about systems, teams or policies.
  • Open questions and decisions: identify unresolved issues, accountable owners and the current approved scope.

This is a suggested format for the example, not a mandatory PRD template. A small change may fit on one page. The document should make uncertainty easy to inspect without burying the proposed product behaviour in raw transcripts.

Make the chosen behaviour verifiable

NASA's foundational requirements checklist stresses clear statements, explicit assumptions, traceability and verification. Those disciplines are useful in software, even when a team uses a lighter document than a systems-engineering programme.

For our proposed invitation-status feature, “make onboarding clearer” is an outcome direction. The team needs to specify which authorised role sees which invitation states, how those states are determined and what appears when information cannot be retrieved.

Agree these details with design and engineering rather than allowing the drafting model to invent the existing system's behaviour. Link the resulting acceptance checks to the requirement identifier. Keep the rationale nearby so a later change can be assessed against the original purpose.

Record decisions and review meaningful changes

A reviewer should be able to accept a proposed scope, request clarification or defer a decision. Record the document version, the decision and its rationale. The distinction between approving a PRD and authorising downstream actions is covered in where human approval belongs in an agentic workflow.

For the example, confirmation of a different invitation-expiry rule could affect the requirement and its acceptance checks. Preserve the earlier version, identify the affected items and return the changed proposal to the relevant owner. Avoid regenerating an entire PRD in a way that silently overwrites accepted scope.

Evaluate fidelity as well as readability

OpenAI's evaluation guidance recommends task-specific checks, representative cases and calibration with human judgment. For this workflow, a polished document is only one part of the evaluation.

Build a reviewed set of source packs containing ambiguous wording, conflicting requests, missing metrics and unsupported feature suggestions. Check whether the output preserves quotation accuracy, source links, uncertainty and decision status. Include cases where leaving a field unresolved is the correct result.

For the onboarding example, separately assess missed evidence, unsupported requirements, lost qualifications and the time a product owner spends correcting the draft. Keep evaluation examples separate from those used to tune the instructions. Review the comparison when the model, prompt or extraction process changes.

Start with one bounded section, such as the problem statement and its evidence. Expand only when reviewers can reliably see what came from the source, what was inferred and what still requires a decision. That distinction is what makes a draft useful for product work.

Sources

Sources checked on 1 November 2026. The GOV.UK pages were published in 2016–2017 and are cited as foundational research guidance, alongside NASA's requirements checklist. Current API documentation was checked for the specific capabilities and limitations discussed. The onboarding scenario, record labels and proposed PRD format are illustrative editorial examples.