You type a prompt into an AI editor, and ten seconds later, you have a working login page. It looks great. It feels magical. Then you try to add password reset logic, and the whole thing collapses because the database schema doesn't match the new UI component. This is the vibe coding trap. It’s fast until it isn’t. The solution isn’t to stop using AI; it’s to change how you manage requirements. Enter Outcome-Driven Development (ODD) within vibe coding projects.

This approach shifts your focus from "what code do I need?" to "what business outcome must this achieve?" By treating requirements as structured artifacts rather than fleeting chat messages, you keep AI assistants like Claude or Gemini on track. You stop generating spaghetti code that works for happy paths but breaks under real-world complexity. Here’s how to actually manage requirements when you’re letting an LLM write the bulk of your application.

Why Traditional Requirement Docs Fail with AI

If you’ve ever tried to hand a 50-page PDF specification to an AI, you know it struggles. Large Language Models (LLMs) don’t read documents linearly like humans do. They look for patterns, context windows, and immediate constraints. When you dump a massive, static requirement doc into a prompt, the model often ignores half of it or hallucinates connections that aren’t there.

In traditional waterfall or even agile setups, we rely on human interpretation to fill gaps. An engineer reads a ticket, talks to a product manager, and makes assumptions. In vibe coding, there is no hallway conversation. If the requirement isn’t explicit in the current context window, the AI guesses. And those guesses are often wrong in subtle ways-like missing role-based access controls or inefficient database queries.

ODD fixes this by breaking requirements down into small, executable outcomes. Instead of asking the AI to "build the user module," you ask it to "implement the login flow with JWT authentication." Each step is a discrete unit of value. This reduces the cognitive load on the model and gives you clear checkpoints for verification.

The Source of Truth: Configuration as Code

One of the biggest failures in early vibe coding was trying to maintain state through conversation alone. You’d tell the AI about your database schema in chat #1, then forget to repeat it in chat #5. By chat #10, the AI had forgotten the foreign key relationships you established earlier.

The fix is establishing a persistent Source of Truth outside the chat history. For many developers, this means leveraging configuration-driven frameworks like Wasp or Next.js config files. These tools abstract away boilerplate and provide a centralized place where architecture lives.

Consider a project using Wasp. Your `main.wasp` file defines routes and operations. Your `schema.prisma` file defines the data model. These files are static, version-controlled, and always available to the AI if you configure your editor correctly. When you ask the AI to generate code, it references these existing definitions rather than inventing new ones. This architectural transparency ensures that every generated line of code respects the pre-defined constraints. You’re not just prompting; you’re guiding the AI within a sandbox you built.

Building the Rule System

You wouldn’t hire a junior developer and expect them to guess your company’s coding standards. Yet, that’s exactly what we do with default AI settings. To manage requirements effectively, you need explicit rule systems. Most modern AI IDEs, like Cursor, support directories like `.cursor/rules/` where you can store markdown files detailing project conventions.

These rules aren’t just style guides. They are operational constraints. They define things like:

• Authentication Patterns: "Always use NextAuth for session management. Do not implement custom JWT logic unless specified."
• Error Handling: "All API responses must follow the standardized error envelope structure defined in `utils/errors.ts`."
• Database Access: "Never query the database directly in components. Use server actions only."

By encoding these rules, you prevent the AI from oscillating between different implementation strategies. It stops writing one feature with Redux and the next with Context API. The rules act as guardrails, ensuring consistency across hundreds of generated lines of code. Treat these files as living documents. If the AI keeps making the same mistake, update the rule. This iterative refinement is part of the ODD loop.

The PRD and Stepped Implementation Plan

Here is where most people get lazy. They skip the planning phase and jump straight to coding. Don’t. Before you let the AI touch a single file, create a lightweight Product Requirements Document (PRD). But unlike traditional PRDs, this one is designed for machine consumption.

Start with a high-level description of the feature. Then, ask the AI to generate a stepped implementation plan. For example, if you want to build a shopping cart, the AI might break it down into:

1. Define CartItem entity in schema.prisma.
2. Create AddToCart operation in main.wasp.
3. Build the AddToCartButton component.
4. Connect the button to the operation via server action.

This plan becomes your roadmap. You execute it one step at a time. After each step, you verify the outcome before moving to the next. This prevents the "big bang" integration problem where you generate 500 lines of code and then spend two days debugging why nothing connects.

Vertical Slices Over Horizontal Layers

Traditional development often builds horizontally: all the database models first, then all the backend APIs, then all the frontend UI. In vibe coding, this is a disaster. Why? Because the AI loses sight of the end goal. It might create a perfect database schema that doesn’t actually support the UI needs you haven’t built yet.

Instead, use Vertical Slices. Implement complete features from top to bottom. Start with authentication. Not just the DB table, but the signup form, the validation logic, the session storage, and the protected route check. Once that slice works, move to the next one, like profile editing.

This approach forces you to validate requirements early. If you try to build the entire database layer first, you might realize three weeks later that your UI needs a field you forgot to include. With vertical slices, you catch these mismatches immediately. The feedback loop is tight. You see working software after every few prompts, which keeps motivation high and errors low.

Documentation as a Feedback Loop

Code rots. Requirements change. In a vibe coding environment, knowledge silos form quickly because the AI generates code faster than humans can read it. To combat this, make documentation part of the definition of done.

After completing a vertical slice, set up a workflow where the AI generates its own documentation. Create an `ai/docs/` directory. Ask the AI to summarize the completed feature based on the code it just wrote. It should explain the core logic, how the components connect, and any architectural decisions made.

This serves two purposes. First, it creates a human-readable record for future reference. Second, it acts as a self-check. If the AI can’t clearly document the logic, the logic is probably too complex or poorly structured. You can then refactor before moving on. This meta-cognitive step turns documentation from a chore into a quality assurance tool.

Handling Enterprise Complexity

Vibe coding shines for startups and MVPs, but enterprises have stricter needs. Security, compliance, and scale don’t care about vibes. Standard AI prompts often produce insecure defaults. For instance, an AI might generate an API endpoint without rate limiting or proper CORS configuration.

To manage these requirements, you need explicit governance. Define security rules in your rule system. Require manual review for any code touching sensitive data. Use platforms that offer observability, like CloudFlare’s VibeSDK or enterprise-grade no-code tools, which provide audit trails and role-based access out of the box.

Remember, speed is not the only metric. Architectural coherence matters more in the long run. If your app runs fast but crashes under load due to inefficient queries generated by the AI, you’ve traded short-term velocity for long-term debt. Outcome-Driven Development mitigates this by forcing performance considerations into the planning phase. Ask the AI to consider indexing strategies or caching mechanisms during the design step, not just the coding step.

Practical Workflow Example

Let’s walk through a concrete scenario. You need to add a "Dark Mode" toggle to your app.

1. Define Outcome: Users can switch between light and dark themes, persisted across sessions.
2. Update Rules: Ensure your CSS variables setup is documented in `.cursor/rules/ui.md`.
3. Generate Plan: Ask the AI for steps. It suggests: Update Tailwind config, create ThemeProvider, add toggle component, persist preference in localStorage.
4. Execute Slice: Implement one step at a time. Verify the theme changes visually.
5. Document: Ask the AI to write a snippet for `docs/theme-system.md` explaining how the provider works.
6. Critique: Paste the new docs back into the chat and ask, "Are there edge cases I missed? What about users with 'system' preference?"

This loop ensures that every feature is intentional, tested, and documented. It transforms vibe coding from a chaotic experiment into a disciplined engineering practice.