How to prompt so it actually builds what you meant.

Before using Lovable, define what you're building. Skipping this is like starting a painting without deciding what you're painting. Answer four questions: What is this product or feature? Who is it for? Why will they use it? What is the one key action the user should take?

You're not writing a spec doc, you're setting direction. Vague ideas produce vague outputs. Use Plan mode for this stage — you can talk through ideas and let the AI ask smart questions without touching code.

Design isn't about individual screens, it's about what happens between them. Map the journey from landing on the page to completing the key action.

Think in transitions: What does the user see first? What builds trust? What gives them confidence to act? Where does that action lead? Even a simple 3-step sketch — Hero → Features → CTA — makes your prompts far more effective.

Your visual language is a foundation, not a polish layer. Lovable needs to know the look and feel early, otherwise your interface might function but feel totally wrong. Don't fix design problems later, decide them up front.

Choose a direction — calm and elegant, bold and disruptive, premium and sleek — then feed it into your prompt using buzzwords, tone descriptors and UI patterns.

Lovable works best when you build your UI in modular parts, not full pages at once. Asking it to generate an entire landing page is like dumping a recipe into a blender.

Approach prompts like Lego bricks: a hero section, a feature grid, a testimonial slider, a pricing table. Build one, review it, refine it, then move on. If something looks wrong you fix that block instead of re-prompting the whole page.

Lovable does not work well with placeholder content like lorem ipsum or "feature 1 / feature 2". The model responds to structure and intent, and the fastest way to show intent is real words.

Even if your final copy isn't ready, write what a user would actually read. A real headline might need two lines instead of one. A CTA might work better as a verb than a noun. Placeholder text hides those issues, real content reveals them early.

Lovable thinks in atoms. The smaller and more specific your UI language, the better it performs. Instead of "a section with a signup", say "a form with an input field for email and a rounded CTA button".

Describe cards, badges, toggles, chips, form fields, dropdowns, toasts. This atomic vocabulary also lets you layer complexity gradually — start with a card, add a badge, then add hover states.

It's not enough to describe layout, you need to guide the tool on vibe. Lovable understands terms like minimal, expressive, cinematic, playful, premium and developer-focused. These aren't fluff — they're promptable parameters that influence typography, spacing, shadow, border radius and colour palette.

Use them early and include them in every section to avoid the default UI look. You can mix and match: a page can be bold and disruptive in the hero and calm and reassuring in the pricing section.

Every prompt modifies a living codebase. Tell Lovable what to build, where to build it, and what to leave alone. Use the Location + Behavior + Guardrails formula.

Prompts are easier to write and far more effective when you use structured, repeatable patterns. Think of them as layout recipes. A good layout prompt breaks the section into visual parts, clarifies their order and defines how they're styled — usually a header → content → action structure.

Write a go-to structure for a feature grid, a testimonial slider, a pricing table, then reuse and remix them. Store these in your project Knowledge file so the agent applies them automatically.

Attach images to any prompt: screenshots of bugs, reference designs, napkin sketches. Use @ to reference specific files in your project or other projects in your workspace. This focuses the AI's attention exactly where you need it.

Drop in product demos, generated clips or tutorial videos using URLs. Prompt for placement (below hero, inside a feature card), style (rounded corners, autoplay, muted) and context (why it's there).

Visual Edit mode lets you make design changes by clicking directly on elements in the preview. Adjust colours, swap fonts, tweak spacing or replace images without writing a prompt. Save your credits for logic, structure and behaviour.

Think of edits as design overrides, not replacements. Be precise: say "replace", "update" or "adjust" instead of "make this better".

The rhythm of good building is prompt, review, prompt, review. Each change should be small enough that you can clearly see what worked and what didn't. If something breaks, you know exactly which prompt caused it.

If you want your projects to do more than look good, think about how they'll actually work. Supabase handles auth, databases, storage and edge functions.

When designing with it in mind, anticipate auth logic (what shows when logged in vs logged out), dynamic content (what's pulled from a table) and states (empty, loading, failed). You don't need a working backend during design, but shaping your UI as if it's already there future-proofs your layout.

As your project grows, periodically ask Lovable to clean things up. Large, messy files confuse the AI. Keeping your codebase modular means the AI can understand and modify each piece more reliably.

Autosave doesn't mean auto-organised. Think in milestones (layout locked, content added, logic wired). Note what changed and why in your prompts. Preview before major changes, and duplicate the project if you're about to take a risky leap. Small cost, big safety net.

The instinct when something breaks is to immediately send another prompt. Resist it. Every prompt you fire at a broken state stacks new changes on top of a fault you haven't identified yet — and now two things are wrong instead of one.

Before anything else, note the last prompt that worked. That's your restore point, and knowing it turns a scary problem into a small one.

If you're not a developer, your guess about what broke is probably wrong — and a wrong guess sends the agent digging in the wrong file. You don't need to know the cause. You need to describe what you saw precisely.

Three things: what you did, what you expected, what actually happened. That's enough for the agent to find it.

Never paraphrase an error. Copy it exactly — the full message from the console or the failed build — and attach a screenshot of the broken screen. Between them, the agent usually finds the fault on the first pass.

One thing that trips people up: when the console shows a wall of red, the first error is the real one. Everything below it is usually a knock-on effect.

Use the automatic fix attempt once. If a second try also fails, stop. A third attempt on the same approach almost never works, and each one adds more changes you'll have to unpick.

Switch to Plan mode and ask for a diagnosis instead of a fix. Making the agent explain the cause before touching anything is the single highest-value habit in debugging.

Narrow down where the problem actually lives before asking for a fix. Four quick checks: does it break on a hard refresh, on a different page, when logged out, and on mobile?

Most breakages in AI-built apps hide in the states nobody prompted for — empty data, still loading, failed request, logged out. If your app works when you're logged in with data and breaks otherwise, that's the answer.

Restoring to the last working version is almost always cheaper than six fix prompts. It costs you one change; the fix spiral costs you an afternoon and leaves the codebase messier than it started.

Go back, then come at the same goal with a smaller, more scoped prompt. You'll usually get there faster than if you'd never broken it.

The same mistakes repeat. Write down one line each time something breaks: what you asked for, and what went wrong. After a handful of entries the pattern is obvious — and it's usually about how you're prompting, not about the tool.

Feed those patterns into your project Knowledge file so the agent avoids them without being told each time.