Assist PRDs and user stories

A model drafts better requirements when it receives a controlled packet instead of scattered context. A useful packet includes:

• the approved problem and scope card;
• research findings with source labels;
• current workflow and baseline;
• policy, legal, security, and accessibility constraints;
• system capabilities and known limits;
• decisions already made, with owners and dates;
• open questions and explicit assumptions;
• terminology that must remain consistent.

Label documents by status: approved, proposed, historical, or superseded. Otherwise, an assistant may blend an old decision with the current plan. Ask it to use only supplied facts and to mark missing information with [OPEN QUESTION] or [ASSUMPTION].

Do not paste sensitive research or confidential strategy into a tool that is not approved for that data. Minimize personal information and follow retention and access rules.

The assistant can create a first-pass structure, but each section serves a different purpose:

Context and problem explain who is affected and why the issue matters now.
Outcome and measures define the change the team seeks and how it will detect benefit and harm.
Scope and exclusions state what the release does and deliberately does not do.
Functional requirements describe observable product behavior.
Quality attributes cover reliability, latency, privacy, accessibility, and supportability.
Risks and controls connect failure modes to prevention, detection, and response.
Rollout and operations define evaluation, release stages, monitoring, fallback, and ownership.

Prompt section by section. Asking for an entire PRD in one generation encourages generic completeness: polished headings filled with invented specifics. A layered process lets reviewers inspect the evidence behind each claim.

Good requirements describe what the system must do under identifiable conditions. Compare:

Vague: “The assistant should provide accurate responses.”
Behavioral: “For eligible returns questions, the assistant must produce an internal draft containing links to the approved policy passages used. If no approved passage meets the retrieval threshold, it must decline to draft and direct the agent to manual search.”

The second requirement identifies eligibility, output, evidence, and fallback. It still needs a tested threshold and definition of “approved,” but those gaps are visible.

Ask AI to scan requirements for weak words such as fast, intuitive, seamless, appropriate, accurate, and secure. It can propose measurable replacements, but the team must choose values based on users, systems, and risk—not on numbers the model invents.

The familiar form—“As a user, I want X, so that Y”—is useful only when the actor and value are specific. Include the actor’s authority and workflow:

As a tier-one support agent handling an eligible returns ticket, I want a draft linked to current approved policy so that I can review and reply without searching several repositories.

Add a separate story for the reviewer or operator when necessary:

As a quality lead, I want sampled drafts and their cited passages available for review so that I can detect unsupported claims and recurring policy gaps.

Avoid combining unrelated actors in one story. A customer’s experience, an agent’s workflow, and an administrator’s controls deserve separate acceptance paths.

Scenario-based criteria make boundaries testable:

Given an eligible ticket and a current approved policy passage,
when the agent requests a draft,
then the system shows a draft, the supporting passage, source title, and source version, and does not send anything.

Also include negative and exceptional cases:

• no approved source is retrieved;
• two sources conflict;
• the ticket requests an out-of-scope refund;
• customer text attempts to override system instructions;
• the source becomes outdated after the draft is generated;
• the service times out;
• the agent lacks permission;
• accessibility technology is used.

AI is especially useful for generating candidate edge cases from a requirement. Review them with engineering, design, operations, and domain owners. Generated scenarios are prompts for discussion, not proof of coverage.

Every important requirement should connect backward to evidence or a constraint and forward to a test, metric, or control. A simple record can include:

• requirement identifier;
• source research or policy;
• decision owner;
• acceptance scenarios;
• launch metric;
• unresolved dependency;
• date and version.

Traceability prevents silent requirement drift. If a policy changes, the team can identify affected criteria. If research does not support a feature, the PM can challenge it. If AI rewrites a story, compare the new version with the approved source rather than trusting fluency.

Generated requirements often add familiar but unapproved promises: “real time,” “all languages,” “personalized,” “industry-leading accuracy,” or “fully automated.” They may imply data access that does not exist or a guarantee that cannot be tested.

Run a commitment review:

1. Highlight every number, deadline, scope word, and guarantee.
2. Identify its source and owner.
3. Check that dependencies and permissions exist.
4. Separate launch requirements from future possibilities.
5. Reject details with no support or turn them into open questions.

Then ask engineering to review feasibility, design to review user behavior, operations to review workflow impact, and relevant risk partners to review controls. AI cannot replace cross-functional acceptance.