Blog / October 28, 2026

Before the First Prompt: 10 Things to Do Before You Start Vibe Coding

vibe-codingagenticbest-practices
D
David
Before the First Prompt: 10 Things to Do Before You Start Vibe Coding

TL;DR: Define the problem, scope your MVP, document expected behavior, choose your architecture, model your data, break the build into phases, write dependency-aware tasks, give your agent a persistent blueprint, and plan your testing. Skipping any of these is how you hit the 80% wall.

Vibe coding makes it easier than ever to turn an idea into a working application. Tools like Lovable, Cursor, Claude Code, and Codex can generate interfaces, write code, and help you build software without requiring you to be an experienced developer. But there's a catch.

Getting an AI coding tool to generate something that works is one thing. Building an application that continues to work as you add features, connect services, and change requirements is another. That's where planning comes in.

Before you start building, you should spend some time defining what you're building, how it should work, and how to break it into manageable steps. This will save you from expensive rework later. You don't need to spend weeks planning. This guide walks through how to plan an application before vibe coding, from the initial idea to a development plan you can give to your AI coding tool.

1. Start with the problem

It's tempting to jump straight in. You want to start creating a dashboard, notifications, payments, an AI assistant, an admin panel, but before deciding what to build, establish what problem the application is supposed to solve.

Answer the following question:

  • Who is this application for?
  • What problem are they experiencing?
  • How do they solve that problem today?
  • What would make your solution meaningfully better?
  • What is the smallest useful version you could build?

For example, imagine you're building an application that helps people organize their travel, your idea might be an AI travel planner with maps, hotel recommendations, restaurant reservations, budget tracking, and personalized itineraries. That's a lot to build in one go. The underlying problem is probably much simpler:

Problem Statement Travelers waste time planning itineraries because they often can't decide on what activities they want to do due to location, time and budget.

This problem statement gives you a clearer starting point to build the first version of your app. A website that lets users enter a destination, dates, budget, and interests, then generate a geographically sensible and budget friendly itinerary.

Once you've validated that the core experience is useful, you can add more features.

The goal: Define the problem and the outcome before asking an AI to build the solution.

2. Define the scope of your Minimum Viable Product (MVP)

An MVP is the smallest version of your application that delivers meaningful value. This is particularly important when vibe coding because while AI tools make generating features feel relatively inexpensive, every feature introduces decisions, dependencies, and more opportunities for something to go wrong.

Create three lists:

Must have: Features required for the application to deliver its core value.

Should have: Features that improve the experience but aren't essential to the first release.

Later: Features you can defer until you know people want them.

For the travel planner example:

Must have Later
Create a trip Collaborate with friends
Enter dates and preferences Social sharing
Generate an itinerary Automated reservations
View activities by day Advanced analytics

You should also identify what your first version will deliberately not do. This prevents an initial build from quietly expanding into a much larger project. A useful rule is that every feature should have a reason to exist in the first release. If you can't explain why it's necessary to validate the core idea, consider deferring it.

3. Write how the application should behave

A feature list tells an AI what capabilities you want. It doesn't necessarily tell it how those capabilities should work. Consider a basic account registration feature. You might need to decide:

  • Can users register with email and password, Google, Facebook, or all?
  • Do users need to verify their email?
  • What happens if they forget their password?
  • What pages can an unauthenticated visitor access?
  • What happens when a user's session expires?

You don't need to document every possible edge case before starting. Focus specifically on decisions that affect security, data, user flows, and the behavior of core features.

For each major feature, describe:

  1. What the user wants to accomplish.
  2. What information they provide.
  3. What the application does.
  4. What the user sees when it succeeds.
  5. What happens when something goes wrong.

For example:

Feature: Create a trip.

Expected behavior: A signed-in user enters a destination, travel dates, budget, and interests. The application validates the input, saves the trip to that user's account, and displays the trip details.

Important edge cases: Invalid dates, missing required fields, failed saves, and attempts to access another user's trip.

This gives your coding tool something more useful than a feature name alone.

4. Decide on the architecture before building

Architecture describes the major parts of your application and how they interact.

You don't need to become a software architect to make useful architectural decisions. You do need to understand the basic components your application requires and how information moves between them.

Depending on your application, these components might include:

  • Frontend: The screens and interface users interact with.
  • Backend: The application logic that processes requests.
  • Database: Where application data is stored.
  • Authentication: How users sign in and what they're allowed to access.
  • External services: APIs for payments, maps, email, AI, or other capabilities.
  • Background jobs: Tasks that run asynchronously or take longer than a typical request.

For the travel planner, a simplified architecture might look like this:

User
  |
  v
Web Application
  |
  v
Backend API
  |
  +----> Database
  |
  +----> AI Service
  |
  +----> Maps / Places API

The diagram is only a starting point. The important questions are how these components communicate, where business rules live, how data is protected, and what happens when an external service fails.

You should also decide which technologies to use. For a simple MVP, a managed backend might be sufficient. A more complex application may need additional services or a different architecture.

Don't add complexity just because a technology sounds impressive. Choose an approach that fits your requirements and that you can realistically build, test, and maintain.

Why this matters: If your AI coding tool invents a different architectural approach for each feature, the application can become inconsistent as it grows. A shared architectural plan gives it a reference point for making implementation decisions.

5. Identify the data model and important relationships

Many application problems start with poorly defined data. Before coding, identify the main things your application needs to store and how they relate to one another. For the travel planner, you might have:

  • Users
  • Trips
  • Trip days
  • Activities
  • Places

A user owns each trip. A trip contains days. Each day contains activities, and activities may reference places. These relationships matter because they influence the database structure, API design, access control, and application logic. You don't necessarily need a complete database schema for every project. But you should identify the core entities, their important relationships, and ownership rules. For example, a user should only be able to access trips they own unless the application explicitly supports sharing. If you leave these decisions entirely to an AI coding tool, it will make assumptions that work for the first screen but may become difficult to change when additional features are introduced.

6. Break the application into implementation phases

Once you understand the requirements and architecture, decide what to build first and what depends on something else. Avoid treating your application as one giant prompt. A more manageable approach is to organize the work into phases.

For example:

Phase 1: Foundation

  • Set up the project structure.
  • Configure the database and environment.
  • Implement authentication.
  • Establish basic error handling.

Phase 2: Core functionality

  • Create and save trips.
  • Capture travel preferences.
  • Generate itineraries.
  • Display activities by day.

Phase 3: Supporting functionality

  • Add map integration.
  • Improve validation and error handling.
  • Add loading and empty states.

Phase 4: Testing and release

  • Test core user journeys.
  • Verify data access controls.
  • Test external service failures.
  • Fix defects and prepare for release.

The order is extremely important in AI assisted building. A feature that depends on authentication, a database table, and an API shouldn't be treated as an isolated task if those dependencies haven't been implemented. The same principle applies within each phase. Build in an order that reduces rework and allows you to test meaningful pieces of the application as you go.

7. Turn the plan into actionable tasks

A roadmap is useful for understanding the overall project, but it isn't always detailed enough for an AI coding agent to execute reliably. Each implementation task should be small enough to understand, implement, and verify. A good task should explain:

  • Objective: What needs to be accomplished?
  • Context: Which part of the application does it affect?
  • Requirements: What behavior is expected?
  • Dependencies: What must exist first?
  • Acceptance criteria: How will you know it works?
  • Constraints: What must not change?

Compare these two prompts.

Too vague:

"Build authentication so the users can login with their emails."

More actionable:

"Implement email and password registration using the project's existing authentication provider. Validate required fields, display useful errors for invalid input, and redirect successfully registered users to the dashboard. Do not create a separate password-storage mechanism. Add tests for successful registration, invalid input, and duplicate email addresses."

The second prompt gives the coding agent clearer boundaries and a way to verify the result. You can use the same approach for database changes, API endpoints, user flows, and integrations.

Not every task needs a long specification. The level of detail should match the complexity and risk of the work.

8. Give your coding agent a persistent blueprint

Even a good initial prompt doesn't guarantee that an AI coding agent will retain every architectural decision throughout a long project. As the codebase grows, conversations become longer, implementation decisions accumulate, and new prompts may omit important context. Different agents or sessions may also make inconsistent assumptions.

Give the agent a persistent source of truth that lives with your project. Depending on your workflow, that could include:

  • An architecture document describing the system and its major components.
  • Project instructions explaining conventions and constraints.
  • A phased implementation plan.
  • A task list with dependencies and acceptance criteria.
  • Decisions about technologies, data ownership, and integration patterns.

Keep these documents current as the application changes. A blueprint that no longer reflects the code can be worse than having no blueprint at all. For tools that support project-level instructions or repository context, put the relevant guidance where the agent can access it consistently. The objective isn't to prevent the agent from making every mistake. It's to reduce ambiguity and give it a coherent set of decisions to follow.

9. Plan how you'll test the application

Testing shouldn't be something you think about only after the AI has finished building. For every important feature, decide how you'll verify it works.

At a minimum, consider:

Happy paths: Does the feature work when users provide valid information?

Error paths: What happens when a request fails, required data is missing, or an external service is unavailable?

Permissions: Can users access only the information they're allowed to see?

Integration behavior: Do the frontend, backend, database, and external services work together?

Regression testing: Does a new change break something that worked previously?

For example, after implementing trip creation, don't stop at seeing the new trip appear on the screen. Check that it was saved correctly, belongs to the right user, survives a page refresh, and handles invalid input.

AI-generated code can look correct while failing under different conditions. A plan should include verification, not just implementation.

10. Use a planning tool when the project becomes too complex to manage manually

For a small application, a checklist and a few well-written prompts might be enough. As the project grows, however, keeping requirements, architecture, dependencies, and implementation tasks consistent becomes harder. You may have to reconcile decisions across several documents and repeatedly explain the same context to your coding agent.

This is where a structured planning workflow can help.

A tool such as Zalcro is designed to turn an application idea or specification into an architecture document, diagrams, a phased implementation plan, and actionable tasks or prompts for AI-assisted development.

Rather than starting with one enormous coding prompt, you can work from a more structured plan and give your coding tool clearer instructions about what to build and in what order.

You can explore Zalcro's sample projects to see examples of the kind of planning output it produces.

You don't need a dedicated tool for every project. The important thing is to have a plan that is detailed enough to guide implementation and easy enough to maintain as the application evolves.

A practical checklist before you start vibe coding

Before asking your AI coding tool to build the application, check that you can answer these questions:

  • Can I explain the problem the app solves and who it's for?
  • Have I defined the MVP and what is out of scope?
  • Have I documented the important user flows and expected behavior?
  • Have I identified the major architectural components?
  • Have I made the important technology and data decisions?
  • Have I identified the core data entities and ownership rules?
  • Have I broken implementation into phases and tasks?
  • Does each important task have clear acceptance criteria?
  • Does my coding agent have access to a persistent project blueprint?
  • Have I planned how I'll test the core functionality and prevent regressions?

You don't need perfect answers to every question before you begin. Some decisions will change as you learn more. The aim is to make the important decisions deliberately rather than leaving everything to the coding agent.

Final thoughts

Vibe coding lowers the barrier to building software, but it doesn't eliminate the need to think through what you're building. The most useful preparation isn't a massive specification that takes longer to write than the application itself. It's a clear definition of the problem, a realistic scope, a coherent architecture, and a sequence of tasks that an AI coding tool can implement and verify.

Start with enough planning to give your agent direction, build in small steps, test as you go, and keep the architectural decisions and project instructions up to date. That way, you're not just asking an AI to generate code, you're giving it a plan to work from.

Plan the system, then build it.

References

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