TL;DR: AI coding tools like Cursor and Windsurf do not read between the lines. They take your brief literally and fill every gap with their own assumptions. A vague PRD produces hallucinated architecture, wrong technology choices, and code that looks right but behaves incorrectly. This guide covers exactly what to put in a product brief so AI agents can implement it faithfully — section by section, with real before/after examples throughout.

Why your current PRD probably does not work with AI coding tools

For the past twenty years, a Product Requirements Document served one audience: humans. Engineers could ask clarifying questions. Designers could check intent in Figma. Whoever wrote the PRD was usually in the same Slack channel as whoever read it.

That assumption no longer holds. In 2026, the first reader of your product brief is often not a person. It is an AI agent inside Cursor, Windsurf, Claude Code, or Bolt that will begin writing production-grade code the moment you submit your prompt.

The problem is structural. Humans interpret context, fill gaps with prior knowledge, and ask questions when something is unclear. AI coding agents do none of these things. They read what you wrote, fill every blank with statistically probable guesses, and generate code accordingly. Those guesses are often wrong.

Three data points that show the scale of the problem:

• 66% of developers report spending more time fixing AI-generated code than writing it themselves when given no spec (Stack Overflow Developer Survey 2025)

• 2.74× more security vulnerabilities are introduced by vibe-coded features versus spec-driven ones (CodeRabbit Report, December 2025)

• 40% reduction in hallucinations when AI agents are given structured specs with explicit constraints (Augment Code, 2026)

The industry has a name for what happens without a spec. It is called vibe coding: you describe what you want in loose natural language, the AI generates something, you iterate until it feels right. Andrej Karpathy, the researcher who coined the term in 2025, scoped it explicitly to "throwaway weekend projects." It was never meant for production software.

The alternative is called spec-driven development: write a structured, unambiguous brief before touching the agent. The brief becomes the single source of truth that constrains what the AI can build. Every implementation decision the agent makes must be traceable back to something you specified.

Common mistake: Most product managers write briefs optimised for a stakeholder presentation, not for an AI agent. These documents are full of narrative prose, qualitative intent language ("seamless experience," "intuitive interface"), and missing technical detail. An AI agent will ignore the narrative and invent the missing details.

What AI coding tools actually read

Before writing a single section of your brief, it helps to understand how Cursor and Windsurf actually process your documents.

How Cursor reads your brief

Cursor is built on VS Code and uses a context assembly system that pulls from several sources in order of priority: your .cursorrules file (project-level instructions), any file you explicitly reference with @filename, the currently open file, and codebase retrieval from the project index. When you ask Cursor to build something, it assembles a prompt from these sources and sends it to the underlying language model.

The practical implication: if your PRD is a PDF in Google Drive, Cursor cannot see it unless you paste it into the chat or reference it as a file in your project. If your PRD is in your project as a Markdown file, Cursor can reference it using @prd.md. Structure it with clear headings and bullet points, because the context retrieval system pulls semantically relevant chunks, not the whole document.

How Windsurf reads your brief

Windsurf uses a different approach through its Cascade agent. Every time you interact with Cascade, it runs through a context assembly pipeline: it loads your .windsurfrules file first, then project memories from previous sessions, then open files (weighted by recency), then codebase retrieval via its M-Query system, then recent terminal and editor actions.

Windsurf's Memories system is particularly important for product briefs. When you define a product constraint in a session ("we are using Supabase for the database, not Postgres directly"), Windsurf can save this as a Memory that persists across future sessions. You do not need to repeat foundational context every time.

What both tools have in common

The pattern is clear. Everything an AI coding agent does well involves following instructions you have already written. Everything it does poorly involves filling gaps you left open.

The anatomy of a brief that AI agents can follow

A brief that works for AI coding tools has eight sections. Each section exists because AI agents need it. None is optional.

1. Problem statement

One paragraph. What user problem are you solving and why does it matter now? This is not for the AI agent directly — it cannot act on a problem statement. But it anchors every decision the agent makes downstream. Without it, the agent optimises for what looks plausible rather than what solves the actual problem.

2. Scope and non-goals

Explicit scope boundaries are more important than feature descriptions. Agents try to be helpful. Without explicit non-goals, they will add authentication when you only wanted a form, build an admin panel when you only needed CRUD, and add mobile responsiveness to something you intended as an internal desktop tool. State what you are not building. It saves hours.

3. Tech stack and constraints

This is the section most product managers skip. It is the section that matters most to AI agents. Specify every technology decision: frontend framework, backend language, database, authentication method, deployment target, API design pattern. If you leave any of these blank, the agent will choose. It will choose differently each time you run the same prompt.

4. User stories with acceptance criteria

Standard user story format works: "As a [user type], I want [action] so that [outcome]." What makes it work for AI agents is pairing every story with explicit, testable acceptance criteria as bullet points. The criteria are what the agent will use to verify its own output. Without them, it has no definition of done.

5. Data model

Name every entity, list every field with its data type, and describe every relationship. If you have a users table, a projects table, and a tasks table, write that out. The agent needs to know the schema before it writes a single query. Without this, it will invent a schema that works for its own interpretation of your requirements, which will not match the schema your team already has in production.

6. API contracts (if relevant)

For any feature that touches an API — internal or third-party — list the endpoints, request/response structures, and authentication method. If your team already has API documentation, link to it and reference it explicitly. The agent cannot guess your endpoint naming conventions or your response envelope structure.

7. Edge cases and error states

Agents are optimistic. They build the happy path. Every edge case you do not specify will be handled with a generic error or ignored entirely. List what happens when inputs are empty, when API calls fail, when users hit rate limits, when permissions are insufficient, and when the data does not exist. This is where most vibe-coded features break in production.

8. Success metrics

Measurable outcomes that tell you whether the feature worked. Not "improve retention" but "users who complete onboarding within 5 minutes increase by 20% in Q3." These give the agent a frame of reference for evaluating trade-offs when it has to make ambiguous decisions during implementation.

Before and after: what the difference looks like in practice

Abstract guidance only goes so far. Here is the same feature written two ways: once as a typical human-readable PRD, and once structured for an AI coding agent.

Feature: user onboarding flow

Before — typical PRD (what most PMs write)

We need a simple onboarding flow that helps new users get set up quickly. It should feel intuitive and guide them through the key steps without overwhelming them. The flow should collect basic profile information and get them to their first value moment as quickly as possible. Design should be clean and consistent with our brand.

An AI agent given this brief will invent a UI framework, choose its own database schema for user profiles, decide what "key steps" means, define "first value moment" based on pattern-matching against other SaaS onboarding flows it was trained on, and write code that looks right but matches none of your actual system architecture.

After — brief structured for AI agents

Problem: 58% of new signups never complete profile setup and churn within 7 days. We believe a guided onboarding flow will improve 7-day retention by reducing friction in the first session.

Scope: 3-step onboarding modal shown on first login only. Steps: (1) name + role, (2) team size, (3) connect first integration (Slack or Jira only).

Not in scope: email verification, profile photo upload, team invites, billing, any integration beyond Slack and Jira.

(continues with tech stack, acceptance criteria, data model, edge cases)

The second version gives the agent a bounded problem, explicit scope, and measurable intent. It can build to this. It cannot build to the first version without inventing most of the product.

The full template: copy, fill in, hand to your agent

Store this file in your project repository as prd.md so agents can reference it with @prd.md. Use Markdown throughout.

File name: prd.md — store this in your project repo so agents can reference it with @prd.md

Feature: [Feature Name]

Status: Draft | In Review | Approved

Last updated: YYYY-MM-DD

Author: [Name, role]

1. Problem statement

One paragraph. What user problem are you solving? Why does it matter now? Include data if you have it.

2. Goals and success metrics

• Primary metric: e.g. Reduce time-to-first-action from 4 min to 2 min

• Secondary metric: e.g. 7-day retention improves from 42% to 55%

• Guardrail metric: e.g. NPS does not drop below current baseline of 32

3. Scope

In scope:

• [Feature / behaviour 1]

• [Feature / behaviour 2]

Out of scope — do not build:

• [Explicit exclusion 1]

• [Explicit exclusion 2]

4. Tech stack and constraints

• Frontend: e.g. React 18, TypeScript, Tailwind CSS

• Backend: e.g. Node.js 22, Express, REST API

• Database: e.g. Supabase (Postgres). Schema in /docs/schema.sql

• Auth: e.g. Supabase Auth, JWT tokens, session stored in httpOnly cookie

• Deployment: e.g. Vercel for frontend, Railway for backend

• Testing: e.g. Vitest for unit tests, Playwright for E2E

• Existing API docs: e.g. See /docs/api-spec.yaml

5. User stories and acceptance criteria

Story 1: As a [user type], I want [action] so that [outcome].

Acceptance criteria:

• [Testable condition 1]

• [Testable condition 2]

• [Testable condition 3]

Story 2: As a [user type], I want [action] so that [outcome].

Acceptance criteria:

• [Testable condition 1]

• [Testable condition 2]

6. Data model

Entity: users

• id: uuid (primary key)

• email: string (unique, from auth)

• full_name: string or null

• role: enum — admin, member, viewer

• onboarding_completed: boolean (default: false)

• created_at: timestamp

7. Edge cases and error handling

• User closes modal mid-onboarding: Save progress to localStorage. Resume on next login.

• Integration auth fails during step 3: Show error with "Try again" option. Allow user to skip.

• Network timeout on form submit: Show "Connection issue" toast. Retry button. Do not clear form.

• User already has onboarding_completed set to true: Skip modal entirely. Do not show again.

8. Open questions

• Do we track onboarding step completion analytics? If yes, what events and where?

• What happens to the Jira integration flow if the user's workspace requires admin approval?

Cursor-specific instructions: the .cursorrules file

In addition to your PRD, Cursor supports a .cursorrules file at the root of your project. This is a plaintext file that tells Cursor how to behave across every interaction in the project. Think of it as your project's standing instructions — the things you would tell a new engineer on day one.

For product teams, a well-configured .cursorrules file means you do not need to repeat foundational context in every prompt. The PRD covers what to build. The .cursorrules file covers how to build it.

File name: .cursorrules — place this at your project root

Project: [Product name]

Last updated: YYYY-MM-DD

Tech stack

• Frontend: React 18 + TypeScript + Tailwind CSS

• Backend: Node.js 22 + Express + REST

• Database: Supabase (Postgres). Always reference /docs/schema.sql before writing queries.

• Auth: Supabase Auth. Tokens in httpOnly cookies. Never localStorage for auth state.

Coding conventions

• Named exports only. No default exports.

• All async functions must have explicit error handling.

• API responses use this envelope: { data: ..., error: null } or { data: null, error: "message" }

• Always validate input at the API boundary before touching the database.

• Write a test for every new function before implementing it.

What NOT to build without asking first

• Do not add new dependencies without listing them as a comment first.

• Do not create new database tables. Propose schema changes as a comment.

• Do not implement authentication flows — already handled in /lib/auth.ts.

• Do not add third-party analytics or tracking code.

Reference files — always check these before implementing a feature

• /docs/prd.md — current feature requirements

• /docs/schema.sql — database schema

• /docs/api-spec.yaml — existing API contracts

• /lib/auth.ts — authentication helpers

Windsurf-specific instructions: rules and memories

Windsurf has an equivalent mechanism called .windsurfrules, which follows the same principle as .cursorrules. The file lives at the root of your project and gives Cascade its standing instructions for the project.

What makes Windsurf different is the Memories system. When you establish a product decision in a Cascade session ("we are not using server-side rendering for this project" or "all API routes go through /api/v2/"), you can ask Windsurf to save it as a Memory. Future sessions load relevant memories automatically before assembling the context prompt.

For product managers working with Windsurf teams, the practical implication is this: briefing sessions matter. The first time you walk through a new feature with Cascade, do it carefully. Ask Windsurf to save the key constraints as Memories at the end of the session. Your team will not need to re-establish that context in every future sprint.

Copilot vs agent mode: Both Cursor and Windsurf operate in two modes — copilot mode (AI suggests, human approves each change) and agent mode (AI plans and executes multi-step changes autonomously). For features you are building from a PRD, start in copilot mode. Review and approve changes incrementally. Only switch to agent mode for well-scoped, well-specified tasks where you can verify a diff before merging.

Copilot briefs vs agentic briefs: the key difference

Not all AI-assisted development is the same. There is an important distinction between asking an AI to help you write code (copilot mode) and asking an AI to write the code for you autonomously (agent mode). Your brief needs to reflect which mode you are using.

The most common brief failures and how to fix them

Failure 1: vague scope

What not to write: "Build a dashboard for tracking team performance."

An AI agent will invent a data model, choose chart libraries, add filters and date ranges nobody asked for, build user management, and potentially include an export feature. You will spend three hours reviewing a 400-line component that is almost but not quite what you wanted.

What to write instead: "Build a read-only dashboard with three metrics: tasks completed this week, average completion time in hours, and open tasks by assignee. Data comes from the existing /api/v2/tasks endpoint. Use Recharts for the bar charts (already in package.json). No filters, no date picker, no export. Single page, no routing."

Failure 2: missing the tech stack

When you do not specify the technology, agents default to what they have seen most often in training data. For frontend this is usually React and TypeScript. For backend it varies. For database it is commonly Postgres or SQLite. These defaults are reasonable but they may not match your actual stack. If your project uses Vue, Prisma, and MySQL and you do not say so, you will get React, raw SQL, and Postgres.

Failure 3: acceptance criteria written as intentions, not tests

Intention (not testable): "Users should be able to log in easily."

Testable acceptance criteria: "User submits valid email and password → redirects to /dashboard within 500ms. User submits invalid credentials → shows 'Incorrect email or password' inline below the form. After 5 failed attempts in 10 minutes → form disables for 15 minutes and shows countdown."

Failure 4: no non-goals section

Agents try to be thorough. Without explicit non-goals, Cursor and Windsurf will add pagination when you wanted a flat list, add mobile responsiveness when your product is desktop-only, and add role-based permissions when you only need a single user type. Every feature takes longer to review, test, and maintain. Say what you are not building.

Failure 5: no reference to existing files

If your project already has an API, a schema, or a component library, the agent needs to know about it. Without explicit file references it will create new files that conflict with what already exists. Add a reference section to your brief with the exact file paths for your schema, API spec, existing auth helpers, and any reusable components the feature should use.

How Squad AI fits into this workflow

The hardest part of writing a brief that AI coding agents can use is not the formatting. It is the upstream work: getting clear on the problem, identifying the right user stories, and connecting the feature back to a measurable business goal. That clarity is what makes every section of the brief faster to write.

Squad AI is built around this upstream workflow. It connects your customer signal sources (Slack, Gong, Intercom, app store reviews, support tickets) to your business goals, surfaces the opportunities with the strongest signal, and produces a one-page brief with goals, acceptance criteria, and dev tasks built in. That output maps directly to the template in this guide. You add the tech stack and edge cases (the parts that require your team's specific knowledge), and the brief is ready to hand to Cursor or Windsurf.

Pre-flight checklist: before you hand the brief to an agent

Run through this before pasting your brief into Cursor or Windsurf.

• Problem statement is one paragraph with data or evidence behind it

• Success metrics are specific and measurable (not "improve experience")

• Scope lists exactly what is in and what is not

• Every technology is named: framework, language, database, auth, deployment

• Each user story has at least two testable acceptance criteria

• Data model names every entity and field with its type

• At least five edge cases and their expected handling are listed

• Existing files the agent should reference are linked explicitly

• A .cursorrules or .windsurfrules file is in the project root

• The brief is stored as a Markdown file in the project repo, not in a Google Doc

Summary

AI coding tools like Cursor and Windsurf are genuinely capable. SWE-bench Verified scores for top models have gone from around 49% in late 2024 to above 80% for the best models in 2026. These tools can now implement production-grade features from a brief with far less hand-holding than was required eighteen months ago.

But their capability is conditional on the quality of what you give them. A vague brief produces hallucinated architecture and code that has to be torn apart and rewritten. A structured brief produces faithful, reviewable implementation in the first pass.

The eight sections in this guide — problem statement, goals, scope, tech stack, user stories with acceptance criteria, data model, edge cases, and open questions — are not bureaucratic overhead. They are the minimum information an AI agent needs to build the right thing. Write them once, keep them in your repository, and you will spend less time fixing code and more time shipping features.

Sources: Stack Overflow Developer Survey 2025 · CodeRabbit Report, December 2025 · Augment Code documentation 2026 · Andrej Karpathy, original vibe coding definition, February 2025 · Thoughtworks Technology Radar, December 2025 · SWE-bench Verified Leaderboard, April 2026 · Windsurf Cascade documentation · ChatPRD research library, March 2026

Last updated: April 2026 · Squad AI