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Eli A
Eli A

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Stop Giving Claude Code Your Whole PRD

#ai #productivity #product #saas

Gaplyze for AI-Native Product Architecting

A PRD is not a good coding-agent task.

It is usually too wide, too mixed, and too full of competing intentions.

A PRD may contain:

product goals
user personas
user stories
edge cases
analytics
technical assumptions
future roadmap ideas
non-functional requirements
launch notes
open questions
design references
success metrics
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That is useful context.

But when you paste the whole thing into Claude Code and say:

Implement this.
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you are not giving the agent a task.

You are giving it a swamp.

Atlassian describes a PRD as a document that defines the product’s purpose, features, functionality, user needs, and success criteria, helping align stakeholders and teams. That is exactly why a PRD is too broad to become a single implementation prompt. It aligns humans across a product decision; it does not automatically decompose work into safe coding slices. (Atlassian)

The better workflow:

PRD
→ build thesis
→ slices
→ task packets
→ agent plan
→ implementation
→ review
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This article is about the middle: turning a PRD into coding-agent tasks without letting the agent inflate scope.

The mistake: treating the PRD like a Jira ticket

A PRD answers:

What are we trying to build and why?
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A coding-agent task should answer:

What exact change should be made now, inside which boundary, with which acceptance criteria?
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Those are different artifacts.

When founders skip the translation step, the agent has to infer:

what is first
what is optional
what is excluded
what is risky
what should be tested
what should remain untouched
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Claude Code can work across a codebase and automate development tasks, but it still needs strong framing and constraints. Anthropic’s Claude Code docs describe it as an agent that understands your codebase and can work across multiple files and tools, which is powerful exactly because vague tasks can spread quickly. (Claude Code)

A broad PRD plus a powerful agent creates one of the most common AI coding problems:

The agent builds a plausible version of the product, not the precise learning slice you needed.

The PRD compiler method

Think of yourself as compiling the PRD into smaller task packets.

A compiler does not throw the whole source into execution blindly.

It parses, resolves, transforms, and emits executable units.

Do the same with your PRD.

1. Extract the product bet
2. Mark the release boundary
3. Split by user journey
4. Convert journeys into task packets
5. Attach acceptance criteria
6. Add explicit non-goals
7. Ask the agent to plan before editing
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This is not bureaucracy.

It is scope control.

Step 1: Extract the product bet

Before coding, reduce the PRD to one sentence:

We are building [workflow] for [specific user] so they can [valuable outcome] because [pain] is frequent or costly.
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Example:

We are building a report-generation workflow for solo B2B consultants so they can turn raw project updates into client-ready weekly reports because manual reporting wastes billable time.
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This sentence becomes the task filter.

Anything that does not support this release should be questioned.

If the PRD contains five possible products, do not ask Claude Code to resolve the strategy while it is also editing files.

Resolve the strategy first.

Step 2: Mark the release boundary

A PRD often describes the vision. A coding task needs the boundary.

Create three boxes:

Now
Not now
Never unless approved
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Example:

Now:
- onboarding questions
- raw update input
- generated report output
- copy/export action
- activation event

Not now:
- teams
- integrations
- billing
- custom templates
- admin dashboard

Never unless approved:
- production database reset
- auth model changes
- pricing logic changes
- new external services
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That third box matters.

Agentic coding can easily touch sensitive parts of the codebase if the prompt is too broad. Anthropic’s newer Claude Code permission work and auto-mode coverage show the same general concern: autonomy is useful, but risky actions still need safeguards and human review. (The Verge)

Do not rely only on the model “understanding” caution.

Write the boundary.

Step 3: Split the PRD by user journey, not by feature list

Most PRDs break work into features.

For coding agents, journeys are usually better.

Feature split:

authentication
onboarding
report editor
report output
analytics
settings
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Journey split:

visitor understands promise
user signs up
user completes setup
user submits raw updates
user receives useful report
user copies/export report
system records activation
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The journey split protects product value.

A feature can be technically complete and still fail the user.

A journey either gets the user to value or it does not.

Step 4: Create task packets

A task packet is the smallest useful unit you give the agent.

Use this shape:

# Task Packet

## Objective
What should be true after this task?

## Product reason
Why does this task matter?

## Scope
What is included?

## Non-goals
What must not be added?

## Files to inspect first
Where should the agent look before planning?

## Acceptance criteria
How do we know this is done?

## Tests / verification
What should be run or added?

## Risk notes
What can break?
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This is much better than:

Implement onboarding from the PRD.
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Example: PRD to Claude Code task

Assume the PRD says:

Users need to enter basic project context before generating their first report.
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Do not give that sentence directly to the agent.

Compile it.

# Task Packet: Onboarding Context Step

## Objective

Add a minimal onboarding step that collects the user's business type, client name, project name, and preferred report tone before the first report is generated.

## Product reason

The report generator needs enough context to produce useful output on the first attempt. This task supports the first-value journey.

## Scope

Included:
- onboarding form
- validation
- persistence to user profile or project context
- redirect to report input screen after completion
- loading, empty, and error states

## Non-goals

Do not add:
- team onboarding
- billing
- integrations
- multi-project dashboard
- advanced preferences
- admin settings

## Files to inspect first

- app/onboarding/*
- app/report/*
- lib/db/*
- components/forms/*
- existing validation utilities

## Acceptance criteria

- User cannot continue with required fields empty.
- Valid input is saved.
- Returning users do not see onboarding again.
- User is routed to report input after completion.
- Mobile layout is usable.
- Errors are visible and recoverable.

## Tests / verification

- Unit test validation schema.
- Add/adjust E2E path: signup → onboarding → report input.
- Run typecheck, lint, and relevant tests.

## Risk notes

Do not change auth behavior.
Do not modify existing report generation logic beyond reading onboarding context.
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That is an agent-ready task.

It is scoped. It is reviewable. It has product reason. It blocks scope creep.

Step 5: Ask for a plan before edits

Do not let the agent jump straight into implementation.

Use this prompt:

Read the task packet and inspect the listed files.

Do not edit code yet.

Return:
1. what you understand the task to be,
2. the relevant product boundary,
3. files you inspected,
4. implementation plan,
5. likely risks,
6. tests you will run,
7. anything ambiguous.

Wait for approval before coding.
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This is especially important for ambiguous or cross-cutting work.

OpenAI’s Codex best-practices guide emphasizes planning, validation, MCP, skills, and reusable guidance as core habits for better results across CLI, IDE, and cloud workflows. (OpenAI Developers)

The lesson generalizes:

Before the agent writes code, make it show the map.

Step 6: Keep reusable guidance out of the task

A task packet should not repeat every repo rule.

Put durable instructions in durable files.

For Codex, OpenAI documents AGENTS.md as a way to give Codex extra instructions and context before it begins work. Codex reads AGENTS.md files before doing any work, allowing global guidance and project-specific overrides. (OpenAI Developers)

For Cursor, project rules serve a similar purpose: Cursor’s docs describe persistent instructions through Project, Team, and User Rules, plus support for AGENTS.md. (Cursor)

A clean setup:

CLAUDE.md                  // Claude Code memory
AGENTS.md                  // Codex-compatible agent guidance
.cursor/rules/product.mdc  // Cursor project rule
docs/tasks/001-onboarding-context.md
docs/tasks/002-report-input.md
docs/tasks/003-report-output.md
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Do not bury permanent rules inside one-off prompts.

Do not bury one-off tasks inside permanent rules.

That separation keeps both clean.

The anti-scope-creep checklist

Before sending any PRD-derived task to Claude Code, check:

[ ] The task has one objective.
[ ] The product reason is explicit.
[ ] The user journey is named.
[ ] Included scope is short.
[ ] Non-goals are explicit.
[ ] Sensitive systems are protected.
[ ] Acceptance criteria are testable.
[ ] Verification steps are listed.
[ ] The agent must plan before editing.
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If any box fails, the task is not ready.

The PRD should not disappear

Do not confuse decomposition with deletion.

The PRD still matters.

It remains the source for:

product purpose
customer needs
success criteria
stakeholder alignment
open questions
constraints
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Atlassian’s PRD template guidance emphasizes consolidating supporting documentation and anticipating open questions and scope creep; that is the right role for the PRD as a context source. (Atlassian)

But the agent does not need to implement the PRD.

The agent needs to implement a slice derived from it.

A practical repo pattern

Use this lightweight folder:

docs/product/
  prd.md
  current-bet.md
  release-boundary.md

docs/tasks/
  001-onboarding-context.md
  002-report-input.md
  003-report-output.md

docs/verification/
  first-value-journey.md
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Then the prompt becomes:

Read:
- CLAUDE.md
- docs/product/current-bet.md
- docs/product/release-boundary.md
- docs/tasks/001-onboarding-context.md

Do not edit code yet.

Inspect the relevant files and propose a plan.
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This is clean enough for a small founder repo and structured enough to avoid chaos.

If the product context is still not clear enough to create task packets, start upstream. A workspace that can generate roadmap slices from product blueprints is useful at that exact point: not to replace engineering judgment, but to turn messy PRD-level thinking into scoped implementation units before an agent starts editing code.

The final rule

Never ask an AI coding agent to “implement the PRD.”

Ask it to implement one task packet derived from the PRD.

The PRD is the map.
The task packet is the route.
The agent is the driver.
You are still responsible for the destination.

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