Vibe Speccing: How AI-Generated Specs and Diagrams Stop Coding Chaos

You know the feeling. You sit down to code a new feature, type a quick prompt into your AI assistant, and watch it churn out lines of code in seconds. It looks good. It compiles. But an hour later, you realize the logic doesn't fit your existing database schema, or worse, it breaks a critical dependency elsewhere in the system. This is the classic trap of "vibe coding"-fast, exploratory prompting that often leads to fragile, hard-to-maintain messes.

There is a better way. Enter vibe speccing, which is a workflow where developers use AI to generate structured requirements, specifications, and design diagrams before writing any implementation code. Instead of jumping straight to code, you pause for five to thirty minutes to let the AI help you define the problem, map the architecture, and visualize the data flow. The result? Fewer bugs, less rework, and code that actually fits your system. Let’s look at how this works, why it matters, and how you can start using it today.

What Is Vibe Speccing?

Vibe speccing is essentially a bridge between chaotic "vibe coding" and rigid, old-school waterfall documentation. "Vibe coding" became popular around 2023-2024 as developers started using large language models (LLMs) like GPT-4 or Claude 3 to write entire features by just asking nicely. It’s fast, but it lacks structure. Vibe speccing takes that speed and adds a layer of discipline.

The core idea is simple: spend a little time upfront to create a shared source of truth for both you and the AI. By generating a spec document first, you force the AI-and yourself-to think about constraints, success criteria, and architectural fit before a single line of code is written. Advocates like Luke Bechtel and Erik Hanchett argue that this extra step saves hours of debugging later because the AI isn’t guessing what you want; it’s following a plan you’ve already approved.

The Four-Phase Specification-Driven Loop

To make vibe speccing work, you need a repeatable process. Many teams are adopting a four-phase loop known as Specification-Driven Development (SDD). Here is how it breaks down:

1. Requirements Phase: You ask the AI to draft a `requirements.md` file. This document answers "What are we building?" It includes user problems, success metrics, and explicit acceptance criteria. For example, instead of saying "make a login page," you specify "create a login flow that supports OAuth2 with Google and GitHub, with a 5-minute token expiry."
2. Design Phase: Based on the requirements, the AI generates a `design.md` file. This covers "How will we build it?" It details technology choices, data structures, and crucially, includes diagrams. This is where you define if you’re using serverless functions, microservices, or a monolith.
3. Tasks Phase: The AI creates a `tasks.md` checklist. This breaks the design into small, executable steps, such as "Create API endpoint for /auth/google" or "Write unit tests for token validation." These tasks can be directly imported into tools like Jira or GitHub Issues.
4. Implementation Phase: Only now does the AI write code. And here is the key: it references the previous three documents as ground truth. If the code deviates from the spec, you catch it immediately.

This loop ensures that every piece of code has a clear reason for existing. It turns vague ideas into concrete, testable artifacts.

The Power of AI-Generated Diagrams

One of the most overlooked benefits of vibe speccing is the ability to generate visual aids quickly. Text specs are great, but humans are visual creatures. In the Design Phase, you can instruct your AI assistant to create diagrams using text-based languages like Mermaid or PlantUML.

For instance, you might ask the AI to generate a Mermaid sequence diagram showing how a React frontend communicates with a Node.js API and a PostgreSQL database. The AI outputs a code block that renders instantly in your IDE or Markdown viewer. These diagrams serve two purposes:

• Clarity: They help you spot logical gaps or missing connections before you code them. Did you forget to handle error responses? The diagram will show a broken link.
• Communication: They make it easier to explain complex changes to stakeholders who don’t read code. A clean component diagram is far more persuasive than a wall of text.

Because these diagrams are stored as text files alongside your code, they stay version-controlled and easy to update as your project evolves.

Setting Up Your Workflow in Cursor or Windsurf

You don’t need new software to start vibe speccing. Modern AI-integrated IDEs like Cursor or Windsurf are perfect for this. Here is a practical setup based on Luke Bechtel’s recommendations:

1. Create a Spec Directory: Make a folder called `.cursor/scopes` or `/docs/specs` in your project root. This is where all your requirement and design files will live.
2. Add a Persistent Rule: In your IDE settings, add a rule that says: "Whenever I ask for a feature, first ask me if I want to create a Spec. If yes, generate a Markdown file in .cursor/scopes/FeatureName.md."
3. Use the Interview Mode: When the AI generates the spec, don’t just accept it. Treat it like a conversation. Ask it: "What edge cases did you miss?" or "Does this align with our hexagonal architecture?" Refine the text until it matches your intent.
4. Gate the Implementation: Do not let the AI write code until you explicitly type "GO!" or "Approve Spec." This mental pause prevents accidental execution of flawed plans.

This setup costs nothing but a few minutes of configuration. The payoff is a cleaner, more predictable development process.

Common Pitfalls and How to Avoid Them

Vibe speccing is powerful, but it’s not magic. There are risks if you do it wrong. Here are the biggest traps developers fall into:

• Spec Drift: If you change the code later without updating the spec, the documentation becomes stale. Future AI prompts will rely on outdated info, leading to errors. Treat specs like code: review them in pull requests and keep them updated.
• Hallucinated Constraints: LLMs can invent plausible-sounding technologies or rules that don’t exist. Always manually review the Requirements and Design phases. Check for fake libraries or incorrect API endpoints before proceeding.
• Overhead for Small Tasks: Don’t vibe-spec a simple typo fix or a one-line refactor. That’s overkill. Reserve this workflow for multi-file features, new APIs, or changes that touch core data models.
• Treating Specs as Rigid Contracts: Specs are living documents. If you find a better way during implementation, update the spec. Don’t let bureaucracy slow you down; let clarity speed you up.

Avoiding these pitfalls means staying engaged. You are still the architect; the AI is just your junior developer helping you draft the blueprints.

When to Use Vibe Speccing

Not every task needs a full spec. Knowing when to apply this workflow is key to maintaining productivity. Use vibe speccing when:

• Building New Features: Any feature that requires multiple files, database changes, or external API integrations.
• Integrating Legacy Systems: When working with older codebases, the AI needs context to avoid breaking things. A spec forces it to analyze existing patterns first.
• Team Collaboration: If multiple developers are working on a module, a shared spec ensures everyone builds the same thing.
• Complex Logic: Algorithms, security flows, or state management systems benefit greatly from visual diagrams and explicit acceptance criteria.

Conversely, skip the spec for quick fixes, UI styling tweaks, or isolated utility functions where the risk of architectural mismatch is low.