Beyond PRDs: The Six Product Facts AI‑Ready Product Managers Must Deliver

Traditional product development passes a requirement through a chain of hand‑offs—Product Manager writes a PRD → Designer interprets → Developer interprets → Tester interprets → Product is built. Each hand‑off re‑interprets the intent, causing loss of context. In the AI era the chain should become a shared factual source where the product manager defines goals, rules, and acceptance criteria, and AI, designers, developers, and testers execute together, followed by automated testing and product‑manager decision.

PRDs often span dozens of pages; each role re‑interprets the requirements. If the product context does not evolve, AI will amplify the problem: it can quickly generate artifacts, but also quickly solidify misunderstandings into the product.

1. AI can provide solutions but cannot make trade‑offs

A requirement such as “Create an elderly profile management page” enables AI to generate pages, forms, APIs, and database tables, yet it omits critical questions:

Who faces what problem?

How is it currently solved?

Why is it worth solving now?

What changes after success?

What is explicitly out of scope?

Example: a nursing home needs a unified view of elderly records rather than a single new page. The product goal should be phrased as “Allow authorized staff to see a single, traceable, up‑to‑date elderly record,” with the page being one possible implementation.

AI can suggest solutions once reliable material is provided, but the product manager must still decide which problems to solve and to what extent.

2. Product rules matter more than page descriptions

In the AI era the most valuable product document is the set of rules, not the mock‑up. Using the elderly profile example, a simple state flow might be:

待审核 → 正常 → 暂停服务 → 注销

Accompanied by rules such as:

Identity numbers are unique across the system.

Deletion after cancellation is irreversible.

Cancellation is prohibited when there are active service orders.

Caregivers can only view records within their service scope.

Institution administrators can view data of their own institution.

Different institutions cannot view each other's records by default.

These rules expose hidden pitfalls (e.g., ambiguity about “system‑wide uniqueness,” recovery after suspension, scope of “irreversible,” etc.) that a plain description would miss.

3. Stop describing button placement in text

Low‑density statements like “Place a new button in the top‑right corner; clicking opens a form” are insufficient. A complete deliverable should combine:

Requirement description + Figma/HTML prototype + page states + interaction rules

HTML prototypes convey structure, component hierarchy, and interaction details that coding agents can parse, while still requiring designers and developers to verify accessibility, responsiveness, and engineering standards.

4. Model business objects before designing pages

Product managers need not design the database, but they must clarify business objects and relationships. An initial object list for elderly care might be:

老人
├── 基础档案
├── 家属
├── 健康档案
├── 服务计划
├── 服务订单
└── 评估记录

Beyond a flat list, a domain model must address identity, ownership, cardinality, and lifecycle—for instance, whether a natural person can have multiple institutional records, how health archives are versioned, and how service plans relate to orders.

5. Acceptance criteria must be machine‑verifiable

Instead of vague functional statements, acceptance criteria should be expressed as concrete, observable conditions. For the rule “no duplicate identity numbers within an institution,” a Gherkin‑style scenario is:

Scenario: Create elderly record
Given user is institution admin
And current institution is A
And institution A has no record with the same ID
When creating the record
Then the record is bound to institution A
And the creation redirects to the detail page
And an audit log entry is written

Scenario: Reject duplicate record
Given user is institution admin
And current institution is A
And institution A already has a record with the same ID
When creating the record
Then submission is blocked
And a duplicate‑record warning is shown

These criteria feed directly into test cases, AI‑driven validation tasks, and product acceptance checklists. They prove that the system meets the specification, not that the feature solves the underlying problem.

6. Record why certain decisions were not made

Documenting the rationale behind omitted features prevents future re‑work. Example:

Decision: First version does not support cross‑institution elderly migration. Reason: No explicit demand from current customers; cross‑institution migration adds significant permission and historical ownership complexity; will be designed in a later phase when multi‑institution needs become clear.

This short note answers three key questions: Was it an omission or intentional? What trade‑off drove the choice? Under what conditions should the decision be revisited?

7. Package the six deliverables into a single context bundle

The six deliverables—Problem Definition, Product Rules, Interactive Prototype, Business/Data Model, Acceptance Criteria, Decision Context—can be organized in a version‑controlled directory structure:

product/
├── vision.md
├── domain.md
├── glossary.md
├── requirements/
│   └── elderly-profile.md
├── rules/
│   └── elderly-rules.md
├── prototype/
│   └── elderly-profile.html
├── acceptance/
│   └── elderly-profile.md
└── decisions/
    └── product-decision-001.md

Mapping each deliverable to its location and the questions it answers:

Problem Definition – vision.md + requirements/ – Why are we building this? What problem does it solve?

Product Rules – rules/ – What business, permission, and quality constraints exist?

Interactive Prototype – prototype/ – How should pages behave and interact?

Business & Data Model – domain.md – What objects exist and how are they related?

Acceptance Criteria – acceptance/ – What results count as correct?

Decision Context – decisions/ – Why were particular trade‑offs made?

Performance, security, privacy, audit, compatibility, and data‑retention rules belong in rules/ and acceptance/. Post‑launch metrics, review responsibilities, and observation plans belong in vision.md or specific requirement files.

8. Delivery responsibility is shared

Content ownership is distributed:

Problem definition, scope, trade‑offs – Product Manager (lead)

Prototype & interaction states – Product Manager & Designer

Domain model, data relationships, rules – Product Manager & Developer (joint confirmation)

Acceptance criteria & automated checks – Product, Development, Test

Security, privacy, data‑retention – Product, Development & relevant specialists

Launch metrics & post‑launch review – Product, Data & Business

The product manager owns completeness and consistency of the product facts but does not need to define databases, permissions, security, or test plans alone.

9. The product manager becomes a Context Engineer + Decision Maker

The old hand‑off chain evolves into a loop:

Product Spec
   ↓
Design, Development & Agent execute together
   ↓
Automated tests + product acceptance
   ↓
Limited rollout
   ↓
Data & user feedback
   ↓
Product decisions
   ↓
Update Product Spec

AI can assist in drafting PRDs, creating prototypes, splitting user stories, generating test cases, and even writing code, but the hardest tasks for AI remain: defining the problem, making trade‑offs between conflicting goals, and defining what “done” looks like.

Thus the AI‑native product manager is a “Context Engineer + Decision Maker,” responsible for establishing facts, eliminating ambiguity, recording trade‑offs, and enabling both humans and AI to work from the same source of truth.