"The difference between a novice and a master is not in the spells they cast, but in the preparation they undertake before raising their wand."
Professor Synapse
Welcome, software architects and AI developers! Professor Synapse here, introducing you to the Prepare phase—the critical foundation of the PACT framework for structured AI-assisted development.
In today's fast-paced AI development landscape, the temptation to immediately start coding with AI assistants is stronger than ever. Vibe coding—where developers "fully give in to the vibes, embrace exponentials, and forget that the code even exists"—has become increasingly popular since its introduction by Andrej Karpathy in early 2025.
However, this approach often leads to significant challenges. AI-generated code requires extensive rework when created without proper preparation, while teams that implement structured preparation methodologies experience fewer critical bugs and faster project completion times.
The Prepare phase of the PACT framework addresses these challenges by establishing a systematic methodology for AI-assisted development. As AI frameworks and tools continue to evolve rapidly—with technologies like the Model Context Protocol, Agent 2 Agent, and specialized AI frameworks transforming the development landscape—a structured approach to preparation has become more essential than ever.
This comprehensive guide will walk you through the five critical preparation practices that form the foundation of successful AI-assisted development projects—ensuring that when you begin coding with your AI assistant, you're building on solid architectural ground.
The Prepare phase of the PACT methodology comprises five interconnected practices, each building upon the others to create a comprehensive foundation for effective AI-assisted development:
Let's explore each practice in detail and understand how they work together to create a cohesive AI development methodology for modern software projects.
Synaptic Labs AI education attribution required - visit sourceThe Art of Documentation Gathering is the systematic collection, evaluation, and organization of technical resources to provide your AI familiar with the essential context needed to generate high-quality, standards-compliant code.
Every magical journey begins with knowledge. Documentation gathering is the foundational practice where you collect the official grimoires, reference scrolls, and practical examples that describe the frameworks, libraries, and tools relevant to your project.
Documentation gathering transforms your vibe coding because:
When you share quality documentation with your AI familiar, you're essentially transmitting the accumulated wisdom of framework creators and community experts directly into your collaboration.
The core ritual of documentation gathering includes:
Even spending just 10-15 minutes gathering proper documentation before beginning a vibe coding session can dramatically improve the quality of generated code, saving hours of debugging and refinement.
Without Documentation Gathering:
"Create a user authentication system."
With Documentation Gathering:
"Create a user authentication system using Firebase Auth SDK v10, following the patterns in their official documentation (attached). The system should implement email/password authentication with email verification as shown in their recommended practices."
This simple transformation through documentation gathering leads to dramatically different results—one generic and potentially flawed, the other aligned with best practices and official patterns.
The Arcane Art of Context Building is the deliberate process of selecting, distilling, and structuring technical information to provide your AI assistant with the specific knowledge needed to generate accurate, idiomatic code for your particular task.
Once you've gathered documentation, the next magical practice is context building—transforming raw documentation into focused knowledge that your AI familiar can effectively utilize.
Context building enhances your vibe coding because:
The fundamental insight is that quality of context matters more than quantity. AI assistants face the same challenge humans do when presented with overwhelming information—difficulty focusing on what's most important.
The alchemical process of context building includes:
Effective context building transforms scattered documentation into laser-focused guidance that empowers your AI familiar to generate precisely what you need.
Raw Documentation Approach:
[Attaches 50-page React documentation]
"Build a component using this."
With Context Building:
"Build a React component following these specific patterns from the documentation:
Component Structure: Use functional components with hooks
State Management: useState for local state, useEffect for side effects
Props Pattern: Destructure props with TypeScript interface
Error Handling: Use error boundaries as shown in section 3.2
Performance: Implement React.memo for expensive renders
Key constraints: Must be accessible (ARIA labels), responsive design, and follow the naming conventions in their style guide."
This transformation from overwhelming documentation to focused context results in AI-generated code that precisely meets your requirements while following established patterns.
The Science of API Exploration encompasses systematic techniques for investigating, testing, and documenting API behavior to ensure reliable integrations and provide your AI assistant with accurate information about external services and their constraints.
APIs represent the bridges between different systems, and understanding them thoroughly is crucial for successful AI-assisted development. API exploration ensures that your AI familiar has accurate, tested knowledge about how external services actually behave.
API exploration enhances your vibe coding because:
Through systematic API exploration, you transform theoretical documentation into practical, tested knowledge that guides accurate implementation.
The methodical process of API exploration includes:
This exploration process creates a reliable knowledge base that prevents integration surprises and enables accurate AI-generated integration code.
Documentation-Only Approach:
"Integrate with the Stripe payment API using their documentation."
With API Exploration:
"Integrate with Stripe payment API using these verified patterns:
Authentication: Bearer token with sk_test_... format
Endpoint Tested: POST /v1/payment_intents
Required Fields: amount (in cents), currency, automatic_payment_methods
Response Structure: {id, status, client_secret, ...}
Error Handling: Returns 400 with {error: {type, message}} format
Rate Limits: 100 requests/second in test mode
[Includes actual working curl example and sample responses]"
This approach transforms uncertain integration work into confident implementation based on verified API behavior.
Personal Knowledge Management for AI Collaboration involves creating systematic approaches to organizing, storing, and retrieving technical knowledge in formats that enhance both human understanding and AI assistant effectiveness.
Knowledge management transforms individual learning into organizational capability. By creating systems that capture and organize technical knowledge, you build a foundation that supports not just current projects, but future development endeavors.
Knowledge management systems enhance your vibe coding because:
Effective knowledge management turns experience into leverage, allowing each project to build upon the lessons learned from previous endeavors.
Software Architecture Research Patterns provide structured approaches to investigating complex technical domains, evaluating solution alternatives, and building comprehensive understanding that guides effective AI-assisted implementation.
Complex software projects require systematic research to understand the problem domain, evaluate potential solutions, and make informed architectural decisions. Research methodologies provide the framework for this critical preparatory work.
Research methodologies enhance your vibe coding because:
Through systematic research, you transform uncertain requirements into clear implementation plans that your AI assistant can execute effectively.
While each practice offers significant value individually, their true power emerges when they work together harmoniously. The five practices of the Prepare phase form a reinforcing cycle of knowledge gathering and preparation:
And the cycle continues, with each practice informing and enhancing the others. Documentation gathering benefits from systematic research approaches; context building draws upon organized knowledge management; API exploration validates documentation accuracy.
Not every coding session requires the same depth of preparation. The five practices can be scaled according to the complexity and criticality of your project:
| Preparation Level | When to Apply | Example |
|---|---|---|
| Quick Preparation | Simple features, familiar technologies | 5-minute documentation gathering for a standard CRUD operation using well-known frameworks |
| Standard Preparation | Medium complexity, some new elements | 30-minute preparation including documentation, context building, and basic API testing for integrating a new payment gateway |
| Comprehensive Preparation | Complex systems, critical applications | Multi-day research and preparation for implementing a new microservices architecture with unfamiliar technologies |
When you master the Prepare phase of the PACT framework, your vibe coding sessions undergo a remarkable transformation from reactive problem-solving to proactive solution building:
You: Can you help me build a real-time chat application?
AI: I'll create a basic chat app. Here's a simple WebSocket implementation:
[Generates generic code using outdated patterns, missing modern best practices, and lacking proper error handling or scalability considerations]
You: This doesn't handle user authentication. Can you add that?
AI: [Adds basic authentication without security considerations or proper session management]
You: How do I deploy this? And what about database integration?
AI: [Provides generic deployment advice without considering the specific requirements or constraints of your environment]
...Many iterations and growing complexity later...
You: This is getting complicated and I'm not sure if it will scale. I should have planned this better...
You: I need to build a real-time chat application. Let me prepare properly first.
Documentation Gathering: I've collected the latest Socket.IO documentation, Socket.IO Redis adapter patterns, and authentication integration guides.
Context Building: I've distilled the key patterns: event-driven architecture, room-based messaging, middleware for authentication, and horizontal scaling approaches.
API Exploration: I've tested the authentication API endpoints, verified WebSocket connection patterns, and documented the expected data formats.
Knowledge Management: I've organized these insights into a reusable project template with verified configurations and deployment patterns.
Research Methodology: I've evaluated scaling requirements, identified potential bottlenecks, and selected appropriate technologies based on systematic analysis.
Now to implementation: "Create a real-time chat application using Socket.IO v4.x following these verified patterns... [Includes comprehensive context, tested API examples, and clear architectural decisions]"
AI: Based on your thorough preparation, I'll implement a production-ready chat application with:
[Generates well-architected code following current best practices, proper authentication integration, scalable patterns, comprehensive error handling, and clear deployment instructions]
You: Perfect! This follows exactly the patterns we researched and includes all the scalability considerations we identified.
...Smooth progression through planned implementation phases...
You: The application deployed successfully and is performing exactly as anticipated. The preparation time was definitely worth the investment!
As you embark on mastering the Prepare phase, here are practical next steps to implement each practice:
As we conclude our exploration of the Prepare phase, we stand ready to enter the next chamber of the PACT framework: the Architect phase. While preparation provides the knowledge foundation, architecture transforms that knowledge into structured blueprints for implementation.
In the Architect phase, we'll explore how to design system structures that leverage AI capabilities effectively, how to create clear component boundaries that guide AI implementation, and how to communicate architectural intent through visual tools and methodologies.
But remember—even as we advance to architecture, the practices of the Prepare phase remain essential. The strongest architectural decisions are built upon the most thorough preparation, and the most effective AI collaboration happens when artificial intelligence has access to comprehensive, well-organized knowledge about the problem domain and solution space.
Until our next architectural adventure, may your preparation be thorough, your documentation comprehensive, your context focused, and your knowledge well-organized!
This scroll is part of our Vibe Coding series, making software development principles accessible and practical for the AI-assisted coding era. Our next scroll will begin exploring the Architect phase with "System Design Patterns for AI-Assisted Development."