From Idea to Working App in One Thread

Large Language Models (LLMs) have fundamentally changed how we build software. We’ve moved from asking simple, isolated questions to engaging in continuous, context-aware conversations. This shift has given rise to “prompt patterns”—reusable recipes for guiding an LLM to perform complex, multi-step tasks.

For developers, this is a superpower. By structuring your conversation with an AI, you can create a cohesive development workflow that takes you from a rough idea to a functional application scaffold in a single session. This guide breaks down a powerful pattern for full-stack development, perfect for accelerating prototypes, building MVPs, or just bringing a weekend idea to life.

A prompt pattern is a structured, repeatable method of interacting with an LLM to achieve a specific, high-quality outcome. Think of it like a software design pattern, but for conversational AI. Instead of providing a single, monolithic instruction, you use a series of prompts that build on each other, maintaining context and refining the output at each step.

This approach is the engine behind “vibe coding” or “agentic development,” where the developer acts as a technical lead, guiding the AI through the development lifecycle. The core idea is to maintain a single, continuous conversation where the LLM remembers previous decisions, ensuring that the frontend code it writes tomorrow is perfectly aligned with the database schema it designed today.

This pattern breaks down the development process into four distinct phases, each building on the last. You start with a high-level vision and progressively drill down into technical implementation details.

The magic happens in the continuity. By keeping everything in one thread, the LLM develops a deep understanding of your project’s goals, entities, and relationships. It knows why a user_id field is needed in the todos table because it helped you define the user stories in the first phase.

Here’s a breakdown of the phases:

1. Phase 1: Product definition & scoping. You and the AI act as co-founders, brainstorming the core idea.
2. Phase 2: Data & schema design. You switch to a database architect role, translating features into a data model.
3. Phase 3: API & backend logic. You become a backend developer, generating the API endpoints to manage the data.
4. Phase 4: Frontend scaffolding. You put on your frontend hat, creating the user interface that interacts with the API.

Before writing a line of code, you need a clear plan. This phase is about defining what you’re building, for whom, and what features are essential for the first version. You’ll guide the LLM to act as a product manager.

Start with a broad prompt to establish the persona and goal.

Example prompt:

“Act as an expert product manager. I want to build a simple to-do list application. My goal is to create a minimal, functional prototype. Help me define the core user stories, key features for an MVP, and a single success metric to track.”

The LLM will likely respond with a structured list of user stories (e.g., “As a user, I want to add a task so I can track what I need to do”) and a prioritized feature list (e.g., create, view, complete, delete tasks). This initial conversation sets the foundation for everything that follows.

Why this phase matters:

• Clarity: It forces you to think through the product before diving into technical details.
• Shared context: The AI now understands the purpose behind the features, leading to more relevant technical suggestions later.

Now, you translate those user stories into a concrete data structure. This is where you switch hats from product manager to database architect. Your goal is to create a database schema that supports the features defined in Phase 1.

Example prompt:

“Excellent. Based on the user stories and MVP features we just defined, design a database schema for this application. I plan to use Postgres. Please provide the SQL CREATE TABLE statements for all necessary tables, including primary keys, foreign keys, and appropriate data types.”

The LLM will use the context from Phase 1 to generate the SQL. It knows it needs a users table and a tasks table, and it will likely include a foreign key relationship (user_id in the tasks table) because it understands the application is user-centric.

What you get:

• A solid data model: A ready-to-use SQL schema that directly maps to your product requirements.
• Fewer errors: The AI is less likely to forget fields or relationships because it’s referencing the initial plan.

With a schema in place, it’s time to build the engine. In this phase, you’ll generate the backend API that allows the application to create, read, update, and delete data (CRUD operations).

Example prompt:

“That schema looks great. Now, act as a senior backend developer. Using Node.js and Express, write the complete code for a RESTful API to manage the tasks. It should include CRUD endpoints (POST /tasks, GET /tasks, GET /tasks/:id, PUT /tasks/:id, DELETE /tasks/:id). Assume a connection to the Postgres database is already configured. Include basic error handling and return appropriate JSON responses.”

Because the LLM has the full context of the schema, it will generate code that correctly handles the fields (title, description, is_completed, user_id, etc.). It will create routes, controller logic, and even placeholder data access functions that align with the database design.

Key benefits:

• Speed: Generating boilerplate API code takes seconds, not hours.
• Consistency: The API endpoints will perfectly match the database schema you just created.

Finally, you build the user interface. You’ll ask the LLM to create frontend components that connect to the API endpoints generated in the previous step.

Example prompt:

“Perfect. Now for the final step. Act as a senior frontend developer specializing in React. Create the components needed to interact with the API we just designed.

1. A component to list all tasks (TaskList).
2. A component to add a new task (AddTaskForm).
3. Use the fetch API to call the backend endpoints.
4. Use basic React hooks (useState, useEffect).
5. Apply simple styling with Tailwind CSS for a clean, modern look.”

The LLM will generate React component files (.jsx) with pre-written code for rendering task lists, handling form submissions, and making API calls. The function names, API URLs, and data structures in the frontend code will align with the backend code because it’s all part of the same continuous conversation.

• Start with a persona: Always begin by telling the LLM who to be (e.g., “Act as a principal engineer,” “Act as a product expert”). This anchors its responses in a specific domain.
• Be specific about your stack: Mention the languages, frameworks, and databases you intend to use. The more specific you are, the better the output.
• Review and refine at each step: Don’t just accept the first output. Ask for changes. For example: “Can you add validation to that API endpoint?” or “Refactor that React component to use a custom hook.”
• Know when to take over: The AI’s job is to generate a scaffold. Your job is to take that scaffold, integrate it, write comprehensive tests, and add the complex business logic that makes your application unique.

• It’s for scaffolding, not finishing: This pattern excels at generating boilerplate and well-understood code. It’s not meant to produce a complex, production-ready system out of the box.
• Context windows have limits: On very large projects, the LLM’s context window may fill up. Be prepared to summarize previous steps or break the project into smaller, self-contained threads if needed.
• Always validate the output: LLMs can “hallucinate” or generate code that is subtly incorrect or insecure. Always review and test the generated code as if you wrote it yourself.

Once you’ve scaffolded your app using this pattern, the next logical step is to add user authentication and management. Building secure auth from scratch is complex, time-consuming, and risky. This is where a dedicated service like Kinde fits perfectly into the rapid development workflow.

Instead of trying to prompt an LLM to write authentication logic (which is a significant security risk), you can use Kinde to handle it in minutes.

• Secure by design: Kinde provides a robust, secure, and scalable solution for user login, registration, passwordless authentication, social sign-in, and more.
• Fast integration: With SDKs for popular frameworks like Node.js, React, and Next.js, you can add powerful authentication to your generated scaffold with just a few lines of code.
• Beyond auth: Kinde also provides user management, feature flags, and billing APIs, giving you a comprehensive platform to grow your application from a prototype into a business.

By combining AI-driven scaffolding with a powerful platform like Kinde, you can focus your energy on building the core features that make your product valuable, not on reinventing the wheel.

• Kinde quick start guides
• Node.js SDK
• Express.js SDK
• React SDK