Web Pages & Apps
In 2025, a non-developer can build and deploy a functional web application in an afternoon using AI-powered tools. "Vibe coding" — describing what you want in plain language and having AI generate the code — has matured from a novelty to a practical workflow. This page covers the tools, their limits, and how to get real results rather than broken prototypes.
What You Can Realistically Build
Well within reach (no coding experience needed)
- Landing pages and marketing sites
- Internal tools and dashboards (data tables, calculators, forms)
- Simple CRUD apps (task managers, contact lists, trackers)
- Portfolio and personal websites
- Prototype UIs to show stakeholders before committing to development
- Single-purpose tools: invoice generators, report formatters, converters
Where you will need developer help
- Complex authentication and user management at scale
- Payment processing with full compliance requirements
- High-performance applications with specific latency requirements
- Custom integrations with enterprise APIs and legacy systems
- Applications with complex business logic across many interconnected states
- Anything requiring ongoing maintenance without developer support
Tool Comparison
| Tool | Best for | Deploys to | Tech stack |
|---|---|---|---|
| Lovable | Full-stack apps with database and auth; production-quality output | Lovable hosting / export to GitHub | React + Supabase |
| Bolt (StackBlitz) | Fast prototypes; browser-based; good for demos | Netlify / export code | Vite + React / Vue / Svelte |
| v0 (Vercel) | UI component generation; integrating with existing Next.js apps | Vercel / copy code into project | React + shadcn/ui + Tailwind |
| Replit | Any language; backend-heavy apps; collaborative | Replit hosting | Any (Python, Node, etc.) |
| Claude / ChatGPT (direct) | Single-file tools; simple scripts; explaining and modifying existing code | Manual — copy and paste into your own setup | Any |
The Vibe Coding Workflow
The biggest mistake beginners make is starting too vague and then trying to fix a half-built broken app. A structured approach produces dramatically better results.
- Write a brief before you open the tool. What does the app do? Who uses it? What are the 3–5 core features? What does it look like (colours, layout, tone)?
- Start with one feature, not the whole app. "Build a landing page with a headline, three feature cards, and an email signup form" is better than "build my SaaS app."
- Describe the UI explicitly. Mention layout (sidebar/top nav), colours (dark theme, blue primary), and component types (table, card, modal). AI cannot read your mind.
- Review and iterate in small steps. Accept what works; describe what needs to change specifically. "Move the signup form above the fold" not "make it better."
- Add features one at a time. Get the core working before adding authentication, then data persistence, then third-party integrations. Complexity compounds errors.
- Test on mobile. Responsive design is often broken in AI-generated layouts — check and ask it to fix explicitly.
- Deploy early. Most tools have one-click deploy. Sharing a real URL with stakeholders is more valuable than a perfect local demo.
Effective Prompts for Web Building
Initial build prompt structure:
Build a [TYPE OF APP] for [WHO USES IT].
Core features:
- [Feature 1]
- [Feature 2]
- [Feature 3]
Design: [dark/light theme], [colour palette], [layout style — sidebar nav / top nav / single page].
Data: [where is data stored — local state / database / user uploads].
Do not add features beyond what I listed. Keep it simple and working.
Iteration prompt structure:
The app is working. Make these specific changes:
1. [Specific change — "Change the button colour to indigo"]
2. [Specific change — "Add a delete button to each row in the table"]
3. [Specific change — "Make the nav responsive for mobile with a hamburger menu"]
Do not change anything else.
"Do not change anything else" is critical — AI tools often refactor working code when making small changes, breaking things that were working.
Knowing the Limits
The "it almost works" trap
AI-generated web apps often reach a state that looks complete but has hidden bugs: broken edge cases, security vulnerabilities, non-functional mobile views, or data that resets on reload. Before sharing with users, test every feature deliberately — especially error states, empty states, and mobile. If you cannot read the code to verify it, have a developer do a brief audit before production use.
Choosing between tools
- Need a database + auth? → Lovable (Supabase integration is best-in-class)
- Quick prototype, no data persistence? → Bolt (fastest to running demo)
- Adding a UI component to an existing codebase? → v0 (outputs copy-paste components)
- Backend-heavy or Python-based? → Replit
- Single HTML file tool? → Claude or ChatGPT directly
Getting unstuck
- If the AI is going in circles on a bug, describe the error message exactly — paste the full error text
- Ask it to explain what the broken code is supposed to do before asking it to fix it
- Start a fresh conversation if context has drifted — paste only the relevant broken component
- For layout bugs, describe what you see vs. what you expect in precise visual terms
Checklist: Do You Understand This?
- What is the main difference between Lovable and Bolt — when would you choose each?
- Why is it important to say "do not change anything else" when asking for a specific UI change?
- Write an initial build prompt for a simple internal tool: a task tracker with a list view, the ability to add tasks with a title and due date, and a done/not done toggle.
- What should you test before sharing an AI-built app with real users?
- What is the "it almost works" trap, and how do you avoid it?
- Which tool would you use to add a single React component to an existing Next.js project?