There’s a growing trend in the developer community that we might call “vibe coding”. Jumping into implementation with AI coding assistants like Cursor, typing out prompts, and hoping the generated code is close enough to what you need. It feels productive. It’s fast. And it often works... until it doesn’t.

The problem emerges in that last 20%: when you need to understand the “why” behind a feature, when architectural decisions matter, when business logic intersects with technical implementation. That’s when AI coding tools hit a wall. They’re brilliant at generating code, but they lack the product context that makes code truly useful.

This challenge has become so common that development agencies are seeing a new category of work: “rescue” projects—codebases that were built with AI assistance but need expert intervention to align with actual product requirements. The code works, but it doesn’t solve the right problem.

The solution isn’t to abandon AI coding tools. They’re incredibly powerful. The solution is to give them the structured context they need to generate code that aligns with your product vision. That’s where Codalio PRD comes in.

The Cursor Challenge: Why “Vibe Coding” Fails

When you ask Cursor to “create a REST API endpoint for product reviews,” it generates functional code. But functional isn’t the same as complete. Here’s what gets missed:

Lack of Product Context

AI doesn’t know the “why” behind features. Without understanding user personas, security requirements, or business rules, Cursor makes assumptions. For example, building a user authentication endpoint without understanding:

• Who your users are (verified purchasers vs. anonymous visitors)
• What security requirements apply (GDPR compliance, data retention policies)
• What business rules exist (only purchasers can review products they’ve bought)

The generated code might work, but it won’t match your product’s actual needs.

Architectural Blind Spots

AI generates code without understanding your system architecture. It doesn’t know:

• Your existing data model structure
• Design principles that guide your implementation
• Integration points with other systems
• Scalability requirements

This leads to endpoints that don’t fit your data model, violate design principles, or create architectural inconsistencies that require refactoring later.

Incomplete Requirements

When requirements are vague, AI fills in the gaps with assumptions. Missing validation rules, edge cases, and integration points become technical debt. The code works for the happy path, but fails when real users interact with it in unexpected ways.

The “Rescue” Problem

This is where the “rescue” cycle begins. Agencies receive projects where:

• Code was generated quickly but doesn’t match product requirements
• Features are implemented but miss critical business logic
• Validation rules are incomplete or incorrect
• The architecture doesn’t align with the product vision

The code works, but it needs expert intervention to become production-ready. This is expensive, time-consuming, and frustrating for everyone involved.

How Codalio PRD Solves This

Codalio PRD provides the structured product context that transforms AI coding from “vibe coding” to “informed development.” Here’s how:

Structured Product Context

Codalio’s PRD builder uses specialized AI agents (Project Manager, Designer, Architect, Product Manager) that ask targeted questions to populate PRD sections. As you chat with these agents in the canvas, they generate:

• Elevator Pitch: The core value proposition
• Problem Statement: What problem you’re solving
• Solution: How your product addresses it
• Vision: Where you’re heading

These agents ensure completeness by scoring each section from their unique perspectives. This collaborative planning process ensures code aligns with product goals, not just technical requirements.

User-Centric Design

User personas, journeys, and flows inform implementation. Understanding user needs prevents over-engineering or missing critical features. When Cursor generates code, it can reference:

• Who the user is (persona details)
• What they’re trying to accomplish (user journeys)
• How they’ll interact with the feature (user flows)

This prevents creating features users don’t need or missing features they do.

Technical Specification

ERD diagrams, schema definitions, and sample data provide technical context. This prevents architectural mismatches. When Cursor generates endpoints, it knows:

• The exact data model structure
• Relationships between entities
• Required fields and constraints
• Sample data patterns

Code aligns with your architecture from the start.

Design Principles

Navigation design, sitemap, and design principles guide implementation. This ensures consistency across features. When generating code, Cursor can reference:

• Design patterns used throughout the app
• Navigation structure
• UI component conventions

Features feel cohesive, not bolted-on.

Versioned Planning

PRD versions allow iterative refinement before coding. This prevents the “code now, fix later” anti-pattern.

You can:

• Refine requirements in the PRD
• Get stakeholder feedback
• Score completeness (Project Manager, Designer, Architect, Product Manager perspectives)
• Ensure readiness before generating code

Real Example: Building a REST API Endpoint

Let’s see how this works in practice with a concrete example: implementing a “Create Product Review” endpoint.

Without PRD (Cursor alone)

Developer asks: “Create a REST API endpoint for product reviews”

Cursor generates:

@app.route('/api/reviews', methods=['POST'])
def create_review():
data = request.json
review = Review(
product_id=data['product_id'],
user_id=data['user_id'],
rating=data['rating'],
comment=data['comment']
)
db.session.add(review)
db.session.commit()
return jsonify(review.to_dict()), 201

What’s missing:

• Validation: Can any user review any product? Only purchasers?
• Business rules: What’s the minimum rating? Can users review products multiple times?
• Data relationships: Is there a Purchase model? How do we verify purchase?
• Error handling: What happens if the product doesn’t exist? If the user already reviewed?
• Edge cases: What if the product is discontinued? What if the user is banned?

Result: Code that works but doesn’t match product requirements.

With Codalio PRD (Prevention)

PRD provides:

1. User personas: “Verified Purchasers” who can leave reviews
2. User flows: Review submission flow with validation steps
3. Schema: Review model with relationships (Product, User, Rating)
4. Business rules:

• Reviews must be from verified purchasers
• Ratings 1-5, required
• Comments minimum 20 characters
• One review per product per user

1. User stories: “As a verified purchaser, I want to leave a review so that other buyers can make informed decisions”

Cursor uses PRD context to generate:

@app.route('/api/reviews', methods=['POST'])
@require_auth
def create_review():
data = request.json
user = current_user

# Business rule: Only verified purchasers can review
purchase = Purchase.query.filter_by(
product_id=data['product_id'],
user_id=user.id,
status='completed'
).first()

if not purchase:
return jsonify({'error': 'Only verified purchasers can review products'}), 403

# Business rule: One review per product per user
existing_review = Review.query.filter_by(
product_id=data['product_id'],
user_id=user.id
).first()

if existing_review:
return jsonify({'error': 'You have already reviewed this product'}), 409

# Validation: Rating 1-5, required
if 'rating' not in data or not (1 <= data['rating'] <= 5):
return jsonify({'error': 'Rating must be between 1 and 5'}), 400

# Validation: Comment minimum 20 characters
if 'comment' in data and len(data['comment']) < 20:
return jsonify({'error': 'Comment must be at least 20 characters'}), 400

# Create review
review = Review(
product_id=data['product_id'],
user_id=user.id,
rating=data['rating'],
comment=data.get('comment', '')
)

db.session.add(review)
db.session.commit()

# Update product average rating
update_product_rating(data['product_id'])

return jsonify(review.to_dict()), 201

Result: Code that aligns with product requirements, includes proper validation, respects business rules, and handles edge cases.

With Codalio PRD (Rescue)

Scenario: Existing code has issues (missing validation, wrong permissions)

Process:

1. Load PRD into Cursor context
2. PRD reveals: missing purchaser verification, incorrect rating validation
3. Refactor code using PRD as source of truth
4. Result: Code aligns with product requirements

The PRD acts as a single source of truth, making it clear what needs to be fixed and why.

Best Practices: Using PRD with Cursor

Here’s how to get the most out of this workflow:

Start with PRD

Always generate or refine your PRD before coding. In Codalio, you interact with specialized AI agents in the canvas—each with their own perspective. The Project Manager asks about business goals and success metrics. The Designer explores visual style and user experience. The Architect probes technical constraints and data models. The Product Manager focuses on user needs and market positioning.

Answer their questions thoroughly—they’re designed to uncover requirements you might miss. As you respond, the PRD populates in real-time, showing your progress with scores from each agent’s perspective. Aim for scores above 8 before moving to code generation.

Reference PRD in Cursor

Include PRD sections in Cursor context. When asking Cursor to generate code, reference specific PRD sections:

• “Based on the user personas in the PRD...”
• “Following the user flow for review submission...”
• “Using the ERD diagram from the PRD...”

This gives Cursor the context it needs to generate aligned code.

Iterative Refinement

Update PRD when requirements change, then regenerate code. Don’t just patch code—update the PRD first, then let Cursor regenerate with the new context. This maintains consistency and prevents drift.

Version Control

Track PRD versions to understand requirements evolution. When you need to understand why code was written a certain way, check the PRD version it was based on. This creates a clear audit trail.

Collaborative Planning

PRD scoring (project manager, designer, architect, product manager) ensures completeness. Get scores above 8 before coding. This prevents generating code based on incomplete requirements.

Conclusion

The future of development isn’t “AI replaces developers” or “developers ignore AI.” It’s “AI + structured planning = better outcomes.”

Codalio PRD transforms AI coding from “vibe coding” to “informed development.” By providing structured product context—user personas, business rules, technical specifications, and design principles—PRD enables AI coding tools like Cursor to generate code that aligns with your product vision from the start.

This prevents the “rescue” cycle. It reduces technical debt. It ensures code solves the right problems. And it makes development faster and more reliable.

Planning prevents problems. Structured context enables better AI assistance. Try Codalio PRD with your next Cursor project and experience the difference.