Lovable Tutorial: A Complete Guide to Building Full-Stack Web Apps with AI Conversations from Scratch

What is Lovable?

Lovable is an AI development platform that builds websites and applications through natural language conversations. You simply describe the features you want in plain English (or other languages), and the AI automatically generates code and creates a working application — no programming experience required. Once finished, you can publish it to the internet with a single click. Many people have already used Lovable to build everything from small personal projects to complete software company products.

Lovable falls under the category of "Vibe Coding" tools that have emerged in recent years — a concept proposed by OpenAI co-founder Andrej Karpathy in 2025, referring to developers describing their intent through natural language and relying entirely on AI to generate and maintain code rather than writing it themselves. Lovable's underlying tech stack is based on Large Language Models (LLMs) generating React/TypeScript code, with each conversation triggering incremental code modifications and recompilation. The "live preview" that users see is essentially a continuously running sandbox environment.

This article follows the official tutorial to walk through the complete process of building a fitness application (FitCore) from scratch with Lovable, covering static page construction, database configuration, user authentication, AI feature integration, Stripe payment integration, and other core components.

Step 1: Building Initial Pages with Prompts

Writing a High-Quality Initial Prompt

The first step in building an app with Lovable is writing a sufficiently detailed initial prompt. Using the fitness platform FitCore as an example, a good prompt should include the following elements:

• Page structure: Landing page (with fixed navigation bar), workout library page (categorized by type), nutrition guide page, resource blog page
• Design style: Modern minimalist design, ample whitespace, specified Hex color codes
• Target audience: Clearly inform the AI about the intended user group

A practical tip: you can prepare your prompt in a Google Doc beforehand, or even use ChatGPT to help optimize the prompt's structure and wording. Once ready, simply submit it, and Lovable will automatically generate the first version of your application.

Iterative Optimization: Fixing Issues Through Conversation

The first generated version usually won't be perfect. For example, the "Read Article" button on the blog page might not have navigation functionality. In that case, simply send a new prompt describing the issue and expected behavior:

"Clicking the Read Article button on the resources page does nothing. Please create dynamic pages for each article with paths like /blog or /article, so that clicking a card or button navigates to the corresponding article page."

Lovable will automatically update the code and add this functionality. This conversational iteration approach makes fixing bugs as simple as chatting.

Advanced Features: Custom Knowledge & Visual Editing

Custom Knowledge: Helping AI Remember Global Information

Like all AI tools, Lovable's context memory is limited. All LLM-based AI tools are constrained by the "Context Window" — the maximum text length a model can process in a single inference, typically measured in token count (1 Chinese character ≈ 1.5-2 tokens, 1 English word ≈ 1-2 tokens). As conversation turns increase, earlier messages get scrolled out of the context window, causing the AI to "forget" the project's initial settings.

Custom Knowledge is essentially a "system prompt injection" mechanism: regardless of where the conversation stands, this knowledge text is prioritized and inserted at the head of each request's context, ensuring the AI always has access to the project's core constraints. This shares the same principle as ChatGPT's "Custom Instructions" and Claude's "Project Knowledge" features — it's the mainstream engineering solution for addressing LLM memory limitations. You can persistently store the following key information in the AI's context:

• Complete style guide
• Full feature list
• Target audience description
• Technical architecture requirements

It's strongly recommended to set up Custom Knowledge early in the project and update it promptly after adding new features. You can even use "Chat Only" mode to have Lovable help you generate a comprehensive knowledge file.

Visual Edits: Fine-Tuning the Interface Like a Design Tool

For minor style adjustments (like card spacing or button colors), describing precise pixel values in prompts is both cumbersome and wastes Credits. The Visual Edits feature lets you directly select page elements and manually drag to adjust margin, padding, and colors, with an experience similar to design tools like Figma.

Note that if multiple elements use the same component, modifications will be applied to all instances simultaneously. Additionally, Lovable supports version rollback — you can preview any historical version and restore it with one click, so you never have to worry about breaking things irreversibly.

Enabling Lovable Cloud: Building Full-Stack Applications

Upgrading from Static Website to Dynamic Application

A static website is just the starting point. To implement user registration/login, persistent data storage, API integration, and more, you need to enable Lovable Cloud. It provides a complete backend infrastructure:

• Built-in backend services
• Database storage (based on Supabase)
• User authentication system
• Backend logic (Edge Functions)
• AI integration capabilities

Lovable Cloud's database layer is built on Supabase — an open-source Firebase alternative that provides a PostgreSQL-based relational database, real-time subscriptions, and Row Level Security (RLS). PostgreSQL is one of the most mature open-source relational databases available, supporting complex queries, transactions, and JSON data types. Supabase wraps RESTful APIs and real-time WebSocket interfaces on top of it, allowing the frontend to interact directly with the database without a traditional backend middleware layer. Once enabled, Lovable automatically creates the database schema, including user Profile tables, Exercise tables, and more, associating data with user accounts. The entire process requires no manual database configuration on your part.

Database and Backend Management Panel

Clicking the Cloud icon takes you to the backend management panel, where you can view and manage the following:

• Database: Data tables and their contents — for example, exercises in the exercise table are all associated with the creator's user ID
• Users: List of registered users and their status
• Storage: File storage space for images, videos, and other files
• Edge Functions: Code snippets running on the backend for handling business logic
• Secrets: Securely stored API keys and other sensitive information
• Logs: Debug logs, extremely useful for troubleshooting

Edge Functions are based on a Serverless architecture running on the Deno runtime. Unlike traditional servers, Edge Functions don't need to run continuously — they start on-demand when a request is received and automatically terminate after execution. "Edge" refers to these functions being deployed on edge nodes close to users, reducing network latency. In applications like FitCore, Edge Functions typically handle tasks such as calling third-party APIs, processing Stripe Webhook callbacks, and executing business calculations that require secrecy — operations unsuitable for direct frontend execution since frontend code is fully visible to users.

Integrating AI Coach and External APIs

Adding AI Coach Functionality

Lovable Cloud includes built-in Lovable AI capabilities, allowing you to embed AI assistants in your application. Simply describe the requirement in your prompt:

"Add an AI coach that should be able to access the user's workout plans, progress data, and provide personalized recommendations."

Lovable will automatically recognize this as an AI feature and request to enable Lovable AI. Once configured, the AI coach can read the user's contextual data.