Last Tuesday, David pulled up a blank Lovable project, typed "make me a project management app," hit enter, and then looked at me with the specific expression of someone who has just remembered they forgot to define what "project management app" actually means to them. The AI generated something. It had cards. It had a sidebar. It had absolutely nothing he wanted.

Twenty minutes of vague prompting later, he had a fully functional task tracker with user authentication, a Kanban board, and a dark mode toggle he hadn't asked for but now couldn't imagine living without.

That's the Lovable learning curve in miniature: the tool is genuinely powerful, but the first hour teaches you more about how to prompt than any documentation page ever will. This walkthrough is the documentation page I wish he'd had before that Tuesday.

What Is Lovable (In One Paragraph)

Lovable is a full-stack AI development platform. You describe what you want to build in plain English, and it generates a working application — frontend, backend, database, authentication, and integrations — all backed by real, editable code. It's not a no-code widget-dragger. It writes actual React and TypeScript, connects to Supabase for your database and auth, and syncs everything to GitHub. The fact that you can do all of this without knowing what a React component is remains one of 2026's more remarkable party tricks.

Lovable hit $200M in annual recurring revenue in under a year. The product has opinions, and they're mostly correct.

Prerequisites

Before you start, you'll need:

  • A Lovable account (free tier available, paid plans from $20/month)
  • A Supabase account (free) — if you want a real backend
  • A GitHub account — for code sync and version control
  • One clear idea of what you're actually building

That last one is load-bearing. Lovable will happily build whatever you describe. The more specific the description, the fewer iterations you'll spend correcting the AI's creative interpretations.

Step 1: Write a Prompt That Actually Works

The first prompt you send sets the project's DNA. Lovable uses it to make hundreds of micro-decisions about structure, routing, and component design. A vague prompt produces a vague app that requires ten follow-up prompts to fix. A specific prompt produces something you can actually use in round one.

What not to write:

Build a task management app

What to write instead:

Build a task management app for a small team (2-5 users). Users should be able to log in, create projects, and add tasks with titles, descriptions, due dates, and assignees. Tasks should have three statuses: Todo, In Progress, and Done. Show tasks in a Kanban board layout. Use a clean, minimal design with a white background and blue accents. Include a left sidebar with project navigation.

The difference is every concrete detail you give the AI is a decision it doesn't have to make on its own. Lovable is good at implementation. It is less good at guessing your vision.

For more on this exact problem, David wrote about the vibe coding mistakes that kill Lovable projects after experiencing most of them personally.

Step 2: Use Plan Mode Before Writing Code

One of Lovable's biggest recent updates (February 2026) is Plan Mode — and it changes how you should work with the tool fundamentally.

Before Lovable writes a single line of code, it now shows you a detailed plan of what it intends to build. You can read it, edit it, disagree with specific parts, and approve it before any implementation begins. The approved plan is saved to .lovable/plan.md in your project, so context persists across your entire session.

This is the feature that transforms Lovable from "let's see what happens" into something that more closely resembles intentional product development.

When to use Plan Mode:

  • Any time you're adding a complex feature (authentication, payments, multi-step flows)
  • When refactoring existing functionality
  • When you want to verify the AI understood your request before it rewrites half your codebase

For simple visual changes, you can skip it. For anything structural, use it. The two-minute review saves forty-five minutes of backtracking.

Step 3: Connect Supabase for a Real Backend

Lovable's built-in database is fine for prototypes. For anything production-worthy — or anything with user accounts — you want Supabase connected.

The integration takes about three minutes:

  1. In your Lovable project, click the Supabase icon in the right panel
  2. Connect your Supabase account (OAuth, no API keys needed manually)
  3. Select or create your Supabase project
  4. Lovable automatically configures authentication and database access

Once connected, ask Lovable to add authentication in plain English: "Add user authentication with email/password signup and Google login." It generates the full auth flow, including protected routes, login/signup pages, and session handling. You don't touch a config file.

As of early 2026, Lovable also supports Google and Apple sign-in out of the box. Ask for it and it builds the OAuth flow. It's one prompt, no credential management, no redirect URL configuration from your side.

The Supabase integration documentation covers the full scope — including edge functions, realtime subscriptions, and file storage — if you want to go deeper.

Step 4: Batch Your Work with the Prompt Queue

Here's a workflow change that saves material time: you don't have to wait for Lovable to finish one change before queuing the next one.

The Prompt Queue (also launched February 2026) lets you send multiple prompts while Lovable is still working. They stack in a visible queue above the chat input. You can reorder them, edit individual items, remove ones you've changed your mind about, or pause the entire queue if you want to review an intermediate result before continuing.

For repetitive tasks, there's a repeat feature — you can run a prompt up to 50 times. I haven't found a legitimate use case for that yet, but I respect the commitment to power users.

The practical workflow this enables: while Lovable is building your authentication system, you queue up the dashboard layout, the settings page, and the user profile screen. By the time auth is done, the next three things are already in the pipeline.

Step 5: Test Like a Real User

Lovable's new Browser Testing feature deserves more attention than it gets in most tutorials.

The AI can now interact with your app in a real browser running in a virtual environment — navigate pages, click buttons, fill forms, read console logs, inspect network requests. When you ask Lovable to fix a bug, it can now reproduce the bug itself before attempting a fix, rather than guessing from your description.

This matters more than it sounds. "The login button doesn't work on mobile" is a different kind of bug report when the AI can actually try the login button on mobile and read the resulting error. The fix rate on first attempt improves noticeably.

To trigger it, just describe what you want tested: "Test the checkout flow — add an item to the cart, proceed to checkout, and verify the payment form appears correctly." Lovable runs it and reports what it finds.

Watch this walkthrough to see the full build process in action:

Step 6: Understand Test vs. Live Environments

This is the feature that most changes how you handle going to production.

Lovable Cloud projects now have two isolated environments: Test and Live. Test is where you build. Live is what real users see. The databases are separate. When you publish, your code and database structure go to Live. Your test data stays in Test. Live data never gets overwritten by your experiments.

Before this existed, the standard approach was to be very careful. Now the standard approach is to stop being very careful and let the environments handle it.

It's in beta and free during the beta period. Connect your Supabase project first, then enable it from the project settings. You need to publish at least once before the Live database is created.

This is a big deal for the pattern David and I use: build something, test it properly, ship it. The previous version of that pattern involved more prayer than it should have. See how we evolved beyond pure vibe coding to something more intentional.

Prompting Tips That Actually Make a Difference

After watching David iterate through dozens of Lovable projects (and cleaning up after the ones that went sideways), a few patterns produce consistently better results:

Reference existing UI components by name. "Use a card grid like the one on the dashboard" works better than describing the layout from scratch. Lovable has context about your codebase — use it.

Ask for one thing at a time. "Add a settings page with profile editing, email notifications, API keys, billing management, and team member invitations" will produce something mediocre. Five separate prompts produce five things done properly.

Describe behavior, not implementation. "When a user marks a task as done, move it to the Done column and send a webhook to Slack" is better than "update the task status and make a POST request." Let Lovable decide the technical details.

Review the plan before approving complex changes. Plan Mode exists for a reason. The thirty seconds it takes to read the plan has saved hours of reverting.

Use version history freely. Lovable keeps a full history of every change. There's no cost to experimenting — if something breaks, revert and try a different prompt. The undo button is your friend.

Common Mistakes and How to Fix Them

The AI keeps building the wrong thing. Your prompt is too ambiguous. Add a screenshot reference, a link to a similar product, or describe a concrete user flow step by step.

Authentication isn't working after connecting Supabase. Check that your Supabase project's redirect URLs include your Lovable preview URL. It's in Supabase → Authentication → URL Configuration.

Styles are inconsistent across pages. Ask Lovable to "review the design system and standardize colors, typography, and spacing across all pages." It'll audit and align everything in one pass.

The app looks fine in preview but breaks on mobile. Send the browser testing prompt targeting mobile viewport. Lovable can reproduce and fix responsive issues better now than it could six months ago.

For a more comprehensive breakdown of what goes wrong, the Lovable mistakes post covers the full taxonomy.

What Lovable Can't Do (Yet)

Being honest about the limitations saves you a disappointing afternoon:

  • Complex business logic sometimes requires multiple rounds of iteration and manual code review
  • Third-party API integrations work well for common services (Stripe, Resend, Slack), less reliably for obscure ones
  • Performance optimization for high-traffic apps still needs human attention
  • Mobile apps — Lovable builds web apps, not native iOS/Android (PWA works, though)

The full vibe coding platform comparison puts Lovable in context against alternatives if you're still deciding which tool fits your use case.

What's Next

The most important thing I can tell you about learning how to use Lovable is that the documentation is a starting point, not a ceiling. The tool rewards experimentation. Plan Mode, the Prompt Queue, Browser Testing, Test/Live environments — these aren't features you need to master before shipping your first app. Build something small. Observe how the AI responds to different kinds of prompts. The feedback loop is fast enough that an afternoon of building teaches more than a week of reading about it.

David shipped four internal tools in the month after he figured out the prompting pattern. None of them required a line of code he wrote himself. One of them replaced a manual process that had been wasting two hours a week for two years.

Start with a problem you actually have. That's the Lovable walkthrough that matters.

Want to see what's possible once you're past the basics? Read about vibe coding in 2026 and where the whole category is heading.