Product Strategy • December 2025

How to Structure a PRD for AI Code Generation

AI isn't just assisting developers anymore—it is becoming a primary consumer of product requirements. Here is how to design a next-generation PRD that AI agents can execute with accuracy, determinism, and fidelity.

Why PRDs Must Evolve for AI

For 20 years, PRDs were designed for humans: engineers, designers, and QA teams. But AI-powered IDEs (Cursor, GitHub Copilot Workspace, Replit, JetBrains AI) and MCP-driven agentic workflows change the equation entirely.

AI cannot reliably interpret narrative human prose. It needs structure, constraints, examples, and predicates.

Core Principle

To generate correct code, an AI must receive requirements in structured, machine-readable form, not paragraphs.

This guide walks you step-by-step through designing a modern PRD specifically optimized for AI-powered development using ProdMoh + MCP. This is the new standard for product teams building in an agentic ecosystem.

What Is an AI-Ready PRD?

An AI-ready PRD is a Product Requirements Document that is:

Think of it as the evolution from Word/Notion documents to a requirements API.

Why AI Needs a Structured PRD

Large language models hallucinate when:

AI defaults to “typical patterns.” But your product often requires specific behavior.

A structured PRD removes ambiguity the same way API contracts remove ambiguity from integration work.

The AI PRD Structure (ProdMoh Standard)

This is the canonical structure used by ProdMoh and compatible with MCP-based IDE agents:

  1. User Story (intent)
  2. Acceptance Criteria (predicates)
  3. Examples (input → output)
  4. Technical Constraints
  5. NFRs (performance, latency, security)
  6. Open Questions / Clarifications
  7. Version Metadata

Example (Full AI-Ready Story Object)

{
  "id": "S-204",
  "title": "Apply promo code at checkout",
  "acceptance": [
    {"type":"predicate","expr":"response.status == 200"},
    {"type":"predicate","expr":"response.json.total_after_discount <= response.json.total_before_discount"},
    {"type":"predicate","expr":"response.json.applied_promo == 'WELCOME10'"}
  ],
  "examples": [
    {
      "input": {"cart_total": 100, "promo": "WELCOME10"},
      "output": {"total_after_discount": 90, "applied_promo": "WELCOME10"}
    }
  ],
  "constraints": [
    "Promo codes may not stack",
    "Promo must match regex /^[A-Z0-9]{5,12}$/"
  ],
  "nfr": [
    {"type":"nfr","expr":"latency_p95 <= 300ms"},
    {"type":"nfr","expr":"promo_lookup_time <= 15ms"}
  ],
  "meta": {
    "version": "2025.12.1",
    "author": "pm@company.com"
  }
}

This structure ensures AI agents can produce deterministic, correct code without guesswork.

1. Writing an AI-First User Story

AI doesn’t care about narrative storytelling; it cares about intent, constraints, and outcomes.

Bad (human-only)

“Users should apply coupon codes easily.”

Good (AI-ready)

“User can apply an alphanumeric promo code (5–12 chars) that reduces order total using server-side validation.”

The AI version introduces:

2. Acceptance Criteria as Predicates

Predicates are machine-verifiable statements. AI agents convert these into tests and guardrails.

Bad (vague)

Good (structured)

{"type":"predicate","expr":"discount_amount >= 0"}
{"type":"predicate","expr":"response.status == 200"}
{"type":"predicate","expr":"response.json.applied_promo == input.promo"}

Predicates → test generation → CI enforcement → reliable behavior.

3. Examples: The Missing Ingredient in Most PRDs

Examples are the most powerful component of an AI-ready PRD. They eliminate hallucination completely.

Example

{
  "input": {"cart_total": 150, "promo": "HOLIDAY20"},
  "output": {"total_after_discount": 120}
}

AI uses examples to:

4. Technical Constraints: AI Requires Specificity

Examples of excellent constraints:

AI uses constraints to avoid generating invalid logic or risky patterns.

5. NFRs: A Critical Part of AI PRDs

AI models do not naturally understand performance budgets or security requirements.

Examples

{"type":"nfr","expr":"latency_p95 <= 250ms"}
{"type":"nfr","expr":"throughput >= 400 req/s"}
{"type":"nfr","expr":"must not log PII"}

6. Edge Cases and Negative Scenarios

AI tends to generate happy-path logic. Edge cases prevent silent failures: 

{
  "input": {"promo":"INVALID1"},
  "output": {"error":"PROMO_INVALID"}
}

7. Versioning: PRDs Must Behave Like Source Code

For AI, the PRD isn’t “documentation”—it’s source of truth.

Every story must include:

ProdMoh maintains automatic PRD versioning and MCP exposes the version to IDEs.

MCP: How the PRD Reaches the IDE

Without MCP, developers must copy-paste requirements into IDEs. With MCP, AI agents fetch requirements dynamically based on:

Example:

GET /mcp/prd/prod-2025-v3/stories?id=S-204
Authorization: Bearer <MCP-TOKEN>

End-to-end Workflow (ProdMoh + MCP + IDE)

  1. PM writes structured PRD in ProdMoh
  2. ProdMoh validates predicates, examples, NFRs
  3. ProdMoh publishes PRD version
  4. Developer opens IDE → IDE fetches story via MCP
  5. AI generates tests + mocks from PRD
  6. Developer writes implementation guided by tests
  7. CI verifies PRD version, tests, and invariants

This closes the loop between product intent and engineering execution.

Common Anti-Patterns to Avoid

1. Long paragraphs

Break all logic into predicates, examples, and constraints.

2. Hidden business rules

If AI can’t see it, it won’t implement it.

3. “Easy”, “Fast”, “Intuitive”, “Simple”

These words are poison for AI generation.

4. Missing NFRs

Complete Template for an AI-Ready PRD (Copy/Paste)

{
  "id": "",
  "title": "",
  "acceptance": [],
  "examples": [],
  "constraints": [],
  "nfr": [],
  "meta": {
    "version": "",
    "author": ""
  }
}

Conclusion: PRDs Must Become Machine-Readable

AI code generation will only be as good as the requirements it consumes. If your PRD is vague, your code will be vague. If your PRD is structured, your AI will generate structured, correct, deterministic implementations.

Platforms like ProdMoh provide a practical path to this transformation — turning PRDs into executable, MCP-accessible specifications that power the next decade of engineering workflows.

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