You have your coding companion. Claude Code, Cursor, Windsurf, Copilot, whatever suits the moment.
You ask it to "build a SaaS dashboard," and it delivers something. Perhaps it functions. Perhaps it's only partway there. Or maybe you find yourself circling back for hours, tweaking prompts, feeling that slow drain.
The quiet truth many miss when working with AI: "the clarity of your guidance shapes what emerges." And the most reliable way to frame that guidance? A PRD, a Product Requirements Document.
This isn't some heavy corporate ritual. It's the shift from rough sketches to something solid, ready in a single pass.
What Is a PRD (And Why Does It Matter)?
A PRD lays out a clear path for any builder, human or AI. It settles those core questions:
- "What" are we shaping?
- "Why" does it need to exist?
- "How" does it unfold, step by step?
With human teams, it avoids crossed wires. With AI agents, it curbs those wandering tangents, endless loops, the quiet frustration of rebuilding what you already described.
Picture it like this: an agent without a PRD is a builder handed vague sketches. They'll raise walls, sure. But the hearth might end up where the doorway stood.
The Hidden Toll of Skipping the PRD
Prompt something loose like "build a user authentication system," and watch the drift.
The agent settles on an approach that doesn't quite fit, say email/password when OAuth whispered in your mind. The database drifts from your models. The interface clashes with your app's quiet flow. And suddenly, fixes eat more time than the PRD ever would have asked.
I've guided dozens of MVPs through my agency. The rhythm holds steady: those "30 minutes on a PRD reclaim 3 hours of iteration." Not a hunch. It's pulled from those tight 48-hour builds, where every breath counts.
How to Shape a PRD with AI (Yes, Let AI Help Craft the Guide for AI)
Here's the gentle loop: lean on a chat AI to distill your vision into a PRD, then hand that to your coding agent. A two-breath process that steadies the results.
Step 1: Brain Dump to Claude/ChatGPT
Open a quiet chat with your chosen AI. Spill the raw idea, as if sharing with a trusted collaborator. No polish needed. Just breathe it out.
This prompt template carries it well:
I'm building [product name], a [one-line description].
Target user: [who is this for]
Core problem: [what pain does this solve]
Key features I want in v1:
- [feature 1]
- [feature 2]
- [feature 3]
Tech stack: [e.g., Next.js, Supabase, Tailwind]
Based on this, generate a detailed PRD in structured JSON format that I can
feed to a coding agent. Include: overview, user stories, data models,
API endpoints, UI/page descriptions, auth requirements, and edge cases.Step 2: Choose Your Output Format — JSON or XML
Many guides end here. Yet the shape of your PRD deeply influences how an agent receives it.
"JSON" flows smoothly with most tools today. Structured, parsable, familiar to agents like Claude Code.
"XML" breathes easier in tangled requirements, with its clear nesting and tags. Certain agents hold context better through those open-close markers in longer drifts.
A glimpse of each in motion:
JSON PRD Example
{
"project": {
"name": "YourFavGhostwriter",
"description": "AI-powered LinkedIn ghostwriting tool that learns authentic writing voices",
"tech_stack": ["Next.js", "Supabase", "Tailwind CSS", "Claude API"]
},
"features": [
{
"id": "F001",
"name": "Voice Training",
"priority": "P0",
"description": "System ingests 10+ LinkedIn posts from user to learn their writing style",
"user_story": "As a LinkedIn creator, I want the AI to match my tone so posts feel authentic",
"acceptance_criteria": [
"User can paste or import at least 10 past posts",
"System extracts voice profile: tone, vocabulary, sentence structure, emoji usage",
"Voice profile is stored and applied to all generated content"
],
"data_model": {
"table": "voice_profiles",
"fields": {
"id": "uuid, primary key",
"user_id": "uuid, references users.id",
"sample_posts": "jsonb, array of original posts",
"voice_summary": "text, AI-generated style description",
"created_at": "timestamp"
}
},
"api_endpoints": [
{
"method": "POST",
"path": "/api/voice/train",
"body": "{ posts: string[] }",
"response": "{ voice_profile_id: string, summary: string }"
}
]
}
],
"pages": [
{
"route": "/dashboard",
"description": "Main dashboard showing generated drafts and voice profile status",
"components": ["DraftList", "VoiceProfileCard", "GenerateButton"],
"auth_required": true
}
],
"auth": {
"provider": "Supabase Auth",
"methods": ["magic_link", "google_oauth"],
"roles": ["free_user", "pro_user", "admin"]
},
"constraints": {
"performance": "All pages load under 2s on 3G",
"security": "Row Level Security on all Supabase tables",
"api_limits": "Rate limit AI generation to 20 requests/hour for free tier"
}
}XML PRD Example
<prd>
<project name="YourFavGhostwriter">
<description>AI-powered LinkedIn ghostwriting tool that learns authentic writing voices</description>
<tech_stack>
<technology>Next.js</technology>
<technology>Supabase</technology>
<technology>Tailwind CSS</technology>
<technology>Claude API</technology>
</tech_stack>
</project>
<features>
<feature id="F001" priority="P0">
<name>Voice Training</name>
<description>System ingests 10+ LinkedIn posts from user to learn their writing style</description>
<user_story>As a LinkedIn creator, I want the AI to match my tone so posts feel authentic</user_story>
<acceptance_criteria>
<criterion>User can paste or import at least 10 past posts</criterion>
<criterion>System extracts voice profile: tone, vocabulary, sentence structure</criterion>
<criterion>Voice profile is stored and applied to all generated content</criterion>
</acceptance_criteria>
<data_model table="voice_profiles">
<field name="id" type="uuid">primary key</field>
<field name="user_id" type="uuid">references users.id</field>
<field name="sample_posts" type="jsonb">array of original posts</field>
<field name="voice_summary" type="text">AI-generated style description</field>
</data_model>
</feature>
</features>
</prd>My quiet preference: "JSON." It meets most agents where they are, simple to tend, optimized in their flows. Turn to XML only when nests deepen, calling for those steady tags.
How to Guide Your Coding Agent with the PRD
The heart of it now, how to breathe the PRD into your agent, coaxing exactly your vision.
The System Prompt Formula
Resist just dropping the JSON with a blunt "build this." Give it shape. This is the frame I return to:
You are a senior full-stack developer building a production-ready application.
Here is the complete Product Requirements Document (PRD) for the project:
<prd>
[paste your JSON/XML PRD here]
</prd>
INSTRUCTIONS:
1. Read the entire PRD before writing any code
2. Implement features in priority order (P0 first, then P1, then P2)
3. Follow the exact data models specified, do not add or remove fields
4. Match the API endpoints exactly as defined
5. For each page, implement all listed components
6. Apply all constraints (performance, security, rate limits)
7. Use the specified tech stack, do not substitute libraries
8. Add proper error handling and loading states for every user-facing feature
9. Write clean, typed code with clear file organization
Start with the project scaffold and database schema, then implement features
one by one. After each feature, confirm completion before moving to the next.Pro Tips for Better Results
Break it into breaths. Not the full sweep at once. Divide the PRD into natural pauses, database first, then auth, core flows, surface polish. Agents settle deeper into focused steps than vast horizons.
Reference those feature IDs. With PRD in place, a note like "Now shape F001 — Voice Training" calls it precisely, data in view, paths clear.
Weave in what to leave untouched. "No ORMs, raw SQL alone." "Hold to the PRD, no extras." This guards against sprawl, overreach.
Tuck a decisions log into the PRD. A quiet anchor for choices, keeping the agent's path aligned:
{
"decisions": [
{ "decision": "Use server components by default, client components only when needed" },
{ "decision": "All API routes return consistent { data, error } response shape" },
{ "decision": "Use Supabase RLS instead of middleware for authorization" }
]
}The 30-Minute PRD Workflow
This is the rhythm I follow for each MVP.
- 5 min — Spill the idea raw into Claude's ear. Unfiltered, alive.
- 5 min — Ask it to form the PRD in JSON. Glance over, let it settle.
- 10 min — Tend it gently. Edge cases, data shapes, criteria made clear. Your knowing shapes it here.
- 5 min — Layer in tech bounds, choices, what stays aside.
- 5 min — Part it into phases, set the order.
Total: 30 minutes. And there it rests, turning your agent from scattered spark to steady hand, delivering your outline true.
The Quiet Edge
All chase the next AI tool. Yet the deeper hold lies in the seed, the articulation. A tended PRD parts the weekend tangle from an afternoon's close.
Agents listen well enough. The pause is ours, in naming the shape clearly.
Write the PRD. Give it form. Offer it forward. Let it land.
PRD for coding agents
If you work with lots of ideas and shipping, you have a messy organization — you need a Trello for indie-hackers and SaaS founders. Check out ShipPipe. You can contact me or hire me on LinkedIn.