Aurakl Crevo

Posted on Oct 24

Everyone's Talking About AI Writing Code — But Few Are Asking If It’s the Right Code

#webdev #programming #ai #coding

Everyone’s obsessed with how fast AI can write code.

But after the hype fades, one uncomfortable truth remains:

Speed means nothing if the AI keeps writing the wrong code.

Welcome to the "Vibe Coding" era — where developers prompt their way to entire projects, only to end up with unmaintainable chaos.
The real challenge of AI programming isn’t about generating more code.
It’s about bringing engineering discipline back into the process — and that starts with one thing: the spec.

From Vibe Coding to Specification Collapse: The "Growing Pains" of AI Programming

In 2023, ChatGPT gave us our first taste of AI's coding magic. A short description in plain English, and seconds later, runnable code appeared. The developer community went wild.

In 2024, tools like Cursor and Claude Code took this experience to the extreme. "Vibe Coding" became the new buzzword—programming by "feel." You write a prompt, and the AI generates an entire feature, or even a whole project. This "what-you-think-is-what-you-get" experience made indie devs and startups cheer: "My productivity just 10x'd!"

But the honeymoon phase didn't last.

The moment you use Vibe Coding to build your first real prototype, the problems flood in:

Context is lost: The AI forgets what you asked for last time. Every chat feels like it has amnesia.
Code is inconsistent: The component it builds today looks completely different from the one it built yesterday.
It's impossible to maintain: A week later, you can't even understand the logic the AI generated.
Teamwork is hard: Your AI assistant and your teammate's AI assistant are "speaking different languages."
Changing requirements is a disaster: One small change to a core feature, and the entire codebase needs to be refactored by hand.

This isn’t a tool failure — it’s a process failure.

Vibe Coding is just "improve programming." It's great for fast prototypes and exploring ideas, but in a real engineering project, it's like building a skyscraper with no blueprint.

You might build it fast, but it could fall down at any second.

Specification-Driven Development (SDD): The Engineering Method for AI

There's an old wisdom in software engineering: "Before you write the code, think about what you're trying to do."

In the old days, we wrote requirement docs, design docs, and API specs, then we started coding. But those docs often became a "formality"—just a checkbox. They were written, thrown in a drawer, and the code and the docs slowly drifted into different worlds.

Specification-Driven Development (SDD) flips this relationship:

The code serves the spec, not the other way around.

In an SDD workflow:

The spec is the single source of truth. All development work centers on a shared, versioned specification.
The spec is executable and drives the code. It’s precise enough to generate code and to re-generate it when requirements change.

SDD is especially important in the age of AI, because:

AI needs clear context: Vague prompts give you random results. Clear specs give you predictable results.
Specs can be versioned and reused: The team can share one set of specs to make sure the AI's output is consistent.
Specs close the gap between idea and code: It ensures zero information is lost between "what you want" and "what the AI builds."

But this leads to a huge problem: Who is going to write all these specs?

Writing a complete software design spec can take a team of PMs, architects, and engineers weeks. In a fast-moving startup, that's a cost that's just too high.

This is the exact problem Crevo was built to solve.

Built originally as an internal tool for AI dev teams, Crevo now opens this workflow to everyone.

Crevo: The AI Accelerator for Specification-Driven Development

Crevo is an AI-powered service (based on the MCP protocol) that generates software design documents. Its core mission is simple:

Make writing specs as easy as a conversation, and make Spec-Driven Development a reality for everyone.

Core Feature: Generate Enterprise-Grade Design Specs in 30 Minutes

With Crevo, you just need to:

Describe your product idea (in 200-500 words of plain English).
Crevo automatically generates 7 types of professional design documents:
- User Stories
- Product Requirements Document (PRD)
- System Architecture Design
- Business Flow (Process Design)
- API Design
- Database Design
- Development Plan

Why these 7 documents?

This isn't just about "checking boxes." It's about giving the AI a full, 360-degree understanding of the project.

Document Type	What the AI Understands	How It Guides the Code
User Stories	Who is the user? What do they need?	Generates user-focused feature logic.
PRD	Feature boundaries, priorities, quality.	Defines the scope and "done" criteria.
Architecture	Tech stack, layers, deployment.	Chooses the right frameworks and patterns.
Business Flow	State changes, error handling.	Generates state machines, handles edge cases.
API Design	Interface rules, data formats.	Directly generates RESTful/gRPC code.
Database Design	Data models, relationships, indexes.	Generates the schema and ORM code.
Dev Plan	Task breakdown, dependencies.	Guides the order of development.

Each layer of the spec gives the AI a different set of rules, making sure the final code actually matches what you wanted.

Tech Innovation: More Than Just "Sticking Prompts Together"

Crevo's technology is far more advanced than just "calling GPT to make a doc."

Context-Aware Generation: Keeping all 7 docs in perfect sync.

Crevo uses a session-based system to manage the entire process. When it generates the "Architecture Design," it automatically includes the context from the "User Stories" and "PRD" you already approved. This makes sure your tech choices match your business needs.

How it works: Full chat history, passing context between steps, and AI models that understand the rules from the previous docs.
Multi-Model Teamwork: Using the best AI for each job.

Not all AI models are good at the same things.

PRD Writing: Needs Claude 3 Opus (for stronger logic).
Architecture Design: Needs GPT-4 Turbo (for deeper tech knowledge).
API Design: Needs Gemini Pro (for better structured output).

Crevo lets you set the "best AI expert" for each step, ensuring top quality for every document.
MCP Integration: Generate specs right inside Claude Desktop.

Crevo is built on the Model Context Protocol (MCP). This means you can call Crevo directly from your Claude Desktop:

Set up the Crevo MCP Server in Claude Desktop.
Describe your product idea in the chat.
Claude automatically calls Crevo to generate the specs.
Once the docs are made, Claude can use those specs to start writing the code.

This is true "Conversational SDD"—spec generation and code generation happen in the same workflow.

Why Crevo is the "Best Starting Point" for SDD

Let's compare the options:

Method	What It Generates	The Problem
Writing by Hand	Complete specs	Takes too long (weeks), very high cost.
ChatGPT	A single document	No context, docs don't match each other.
Claude Code (direct)	Just code	Code has no spec layer, hard to maintain.
Crevo	A complete spec system	Fast, consistent, and maintainable.

Crevo's Unique Value:

Speed: 30 minutes vs. 2-4 weeks for traditional specs.
Quality: Pro-level templates + AI expertise.
Consistency: All 7 docs are born from the same context.
Iterative: Supports updating docs and picking up where you left off.
Engineering-Ready: Native MCP support for a seamless AI workflow.

Going Further: Crevo + CLAUDE.md = The Full AI Dev Setup

In the SDD community, there's a popular practice: create a.ai/CLAUDE.md file in your project's root to tell the AI assistant how to behave.

Markdown

# CLAUDE.md## Development Rules-Use TDD-Follow DDD architecture patterns-Error handling must be complete-Code must include comments## Tech Stack-Backend: Go + Kratos-Database: PostgreSQL- Cache: Redis

CLAUDE.md tells the AI how to work (coding style, architecture rules).
Crevo's docs tell the AI what to build (features, API specs, data models).

Your Full Setup:

project-root/
├── .ai/
│   ├── CLAUDE.md          ← AI's "rules and skills"
│   ├── PRD.md             ← From Crevo (What to build)
│   ├── ARCHITECTURE.md    ← From Crevo (How to structure)
│   ├── API_DESIGN.md      ← From Crevo (The interfaces)
│   └── DB_DESIGN.md       ← From Crevo (The data model)
├── src/
└── tests/

Your New Workflow:

Use Crevo to generate the complete spec documents.
Set upCLAUDE.md to define your development rules.
In Claude/Cursor, give a simple prompt:

"Please read all the spec documents in the .ai/ directory and generate the initial project code."
The AI builds high-quality code based on both the specs and the rules.
When requirements change, just update the Crevo specs, and the AI refactors the code.

From Vibe Coding to Spec-Driven: This is the Path We Must Take

In 2023, we were amazed that AI could write code.

In 2024, we learned to prototype fast with Vibe Coding.

In 2025, we need Spec-Driven Development to bring AI programming into the age of engineering.

Crevo isn't here to replace your AI coding tools. It's here to give them a*"brain"*—the clear, complete, and executable specs they need.

When Cursor asks what feature you want, you have a PRD from Crevo.
When Claude needs to understand the architecture, you have an architecture doc from Crevo.
When you need to refactor your code, you just update the spec, and the AI re-generates the code.

This is the true future of AI programming: Humans focus on ideas and decisions. AI handles turning those specs into code.

Specs First, Code Later.

Let's enter the age of AI engineering together!

Resources

🔗 Official Site: https://crevo.aurakl.ai

📖 Docs: https://crevo.aurakl.ai/docs

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