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The Vibe Coding Framework I Use to Build Hackathon Projects Fast — With the Exact Prompts I Used

Who is this article for?

This guide is for you if you're:

  • Building hackathon projects
  • Learning Full Stack Development
  • Using ChatGPT for coding
  • Using Claude Code or Gemini
  • Building MERN Stack applications
  • Building startup MVPs
  • Learning Prompt Engineering
  • Tired of AI breaking your project every third prompt

If you've ever said, "It was working five minutes ago...", 

Congratulations. You're one of us.

Table of Contents

  1. Why Most AI Developers Waste Thousands of Tokens
  2. What is AI Vibe Coding?
  3. The Biggest Myth About AI Software Development
  4. Introducing the AEN Build Loop
  5. Overview of the Seven Stages
  6. Stage 1 — The Genesis Doc
  7. Key Takeaways
  8. What's Coming in Part 2

Why I Wrote This Guide

A few months ago, during a hackathon, I noticed something interesting. A team sitting near us had nearly unlimited AI enthusiasm. Every few minutes someone would say, "Let's just ask ChatGPT.", Five minutes later, "Ask Claude.", Another ten minutes, "Gemini isn't giving good output."

By midnight they had:

  • Three frontend versions
  • Two backend architectures
  • Four authentication systems
  • Zero working application

Meanwhile our project wasn't moving faster because we were better programmers. It was moving faster because our AI wasn't guessing. It was executing. That single realization completely changed how I build software with AI. Instead of treating AI like an all-knowing developer. I started treating it like the world's fastest junior engineer, A brilliant engineer, An incredibly fast engineer.

But still it is an engineer that needs crystal-clear instructions. Everything changed after that.

Why Most AI Developers Waste Thousands of Tokens

Let's be brutally honest. Most people don't actually use AI to build software. They use AI to improvise. Their workflow usually looks something like this.


Open ChatGPT
"Build me a food delivery app."
AI generates 900 lines of code.
"This isn't what I wanted."
"Actually make it modern."
AI rewrites everything.
"The login is broken."
AI fixes login.
The dashboard breaks.
AI fixes dashboard 
Authentication disappears.
Deadline arrives.

Sound familiar? We've all been there. The funny part? It feels productive. The screen is constantly scrolling. Code is constantly appearing. Files are constantly changing. Your CPU fan is working overtime.

Surely you're making progress...,

Right? Not necessarily. Activity is not progress. Generating code is not building software.

The Real Problem Isn't AI

Most people blame the model. GPT isn't good enough, Claude is inconsistent, Gemini hallucinated, Cursor ruined my project.

Sometimes that's true. Most of the time it isn't. Here's the uncomfortable truth.

AI can only build at the level of clarity you provide.

A vague idea produces vague software. A vague prompt produces vague code. A vague architecture produces vague debugging. AI isn't confused. It's following unclear instructions. Imagine hiring a developer and saying:

"Build something cool."

Whose fault is it when they build the wrong thing? Exactly.

AI Is an Amplifier

Think of AI like a Formula 1 car. Put an experienced driver behind the wheel. It's incredibly fast. Put someone who has never driven before behind the wheel...

Speed becomes a liability. AI works exactly the same way. It amplifies your thinking. Good planning becomes incredible execution. Poor planning becomes incredibly fast chaos.


What is AI Vibe Coding?

The internet loves the phrase "vibe coding." Some people use it as an insult. Others use it proudly. Personally, I think the term gets misunderstood.

To me,

AI Vibe Coding is collaborating with AI to build software faster without sacrificing engineering principles.

Notice what I didn't say.

I didn't say:

  • Let AI do everything.
  • Stop thinking.
  • Copy whatever AI generates.
  • Never read the code.

That's not vibe coding. That's outsourcing your brain. Good AI coding is still engineering. You're simply replacing repetitive implementation with intelligent collaboration.

The Biggest Myth About AI Software Development

The biggest myth is:

Better AI = Better Software.

No.

Better thinking = Better Software.

The AI simply accelerates whatever process already exists. If your workflow is chaotic. AI accelerates chaos. If your workflow is structured. AI accelerates execution. That realization eventually became the foundation of the system you're about to learn.

Meet the AEN Build Loop

After dozens of hackathons, side projects, experiments, and more broken AI conversations than I'd like to admit, I eventually noticed something. Every successful project followed almost the same pattern. Every failed project skipped planning. So instead of relying on memory...

I turned the workflow into a repeatable system. I call it the AEN Build Loop. It isn't tied to ChatGPT. It isn't tied to Claude. It isn't tied to Cursor. It works with almost any modern AI coding assistant because it's a thinking framework—not a model-specific trick.

The Seven Stages

Stage-1: Genesis Doc
Stage-2: Blueprint
Stage-3: Forge
Stage-4: Vault
Stage-5: Vault Sprint
Stage-6: Crucible
Stage-7: Launchpad
Output: Finished Product

Every stage exists for one reason.

To remove ambiguity before AI creates it.

Stage 1 — The Genesis Doc

"The quality of your project is determined long before you write your first line of code."

This is the most important stage of the entire framework. Skip it and every later stage becomes harder.

What Is a Genesis Doc?

Think of it as the DNA of your project. It isn't just a list of features. It's the complete blueprint for your idea before implementation begins. Most developers write something like:

  • Login
  • Dashboard
  • Profile Page

Done. That's not planning. That's wishful thinking. A proper Genesis Doc answers questions before AI has the opportunity to guess.

Why This Stage Exists

Every vague requirement becomes five extra prompts later. Every missing feature becomes a redesign. Every forgotten edge case becomes a bug report. Planning feels slow. Rebuilding is slower.

What Beginners Usually Do 

  • Write three bullet points
  • Forget edge cases
  • Ignore user flows
  • Keep changing the tech stack
  • Never define success
  • Ask AI to "figure it out"

What You Should Do Instead

Your Genesis Doc should define:

1. The Problem

What problem does the application solve?

Why should anyone care?

2. Target Users

Who are you building for? The Students, The Doctors, The Developers, The Businesses etc.,

Everyone is not a target audience.

3. Core Features

List every essential feature. Not optional. Not "maybe later." Only the features required for Version 1.4

4. User Flow

How does someone use your application from beginning to end? If you can't explain it... AI won't build it correctly.

5. Technology Stack

Lock it early. Changing from MongoDB to PostgreSQL halfway through a project isn't "being flexible." It's volunteering for extra work.

6. Definition of Done

This is my favorite section. One sentence. That's it.

Example: A student can upload notes, search them within two seconds, generate an AI summary, and download the file without errors.

Now everyone including the AI knows when the feature is actually finished.

Real Example — NoteHive

Throughout this series we'll build a fictional project called NoteHive.
Students can:
  • Upload notes
  • Search notes
  • Filter by subject
  • Generate AI summaries
  • Create quizzes
  • Rate helpful notes

Looks simple. Until you ask better questions.

  • Can duplicate uploads happen?
  • Who moderates copyrighted content?
  • Can PDFs and images both be uploaded?
  • Should anonymous users download notes?
  • Can professors verify notes?

Those questions belong in the Genesis Doc—not during development.

The Exact Prompt I Use

Act as a Senior Product Manager. I want to build NoteHive.
Before writing anything, ask me clarifying questions one at a time. 
until you completely understand my project.
Once finished, create a complete Product Requirements Document that includes:

• Target Users
• Core Features
• User Stories
• User Flow
• Edge Cases
• Tech Stack
• APIs
• Database Suggestions
• Security Considerations
• Future Scope
• Risks
• Definition of Done

Do not assume anything.
Ask questions until you are at least 95% confident.
Pro Tip 💡
  • The best prompt isn't the longest prompt.
  • It's the prompt that forces AI to ask you questions first.
  • Those questions uncover blind spots before they become expensive mistakes.
Key Takeaways
  • AI isn't your architect—you are.
  • Better planning creates better prompts.
  • Every undefined requirement becomes future debugging.
  • A Genesis Doc saves more time than any "magic prompt."
  • The AEN Build Loop begins with clarity, not code.
Coming Next
In Part 2, we'll cover the next three stages of the framework:
  • Blueprint — Turning ideas into an implementation roadmap.
  • Forge — Building efficiently with fresh AI context.
  • Vault — Capturing ideas without destroying your momentum.
These stages are where the framework starts paying off in faster development, 
lower token usage, and far fewer "why did AI change everything?" moments.

Part-2: link
Part-3: link
You can follow me in:  LinkedIn, GitHub, Instagram

— Abhinav, Abhinav Engineering Notes

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