Web Apps with Lovable

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Overview

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Discover how to bring an AI app builder into your team’s real workflow without losing control of quality, security or ownership. In this hands-on course, you’ll learn how to use Lovable to go from a staged brief to a working, published app. You’ll connect it to GitHub so every AI change is tracked and reviewable, and build the shared conventions, review habits and guardrails that let a whole team build with AI safely. You’ll work through practical labs and realistic role plays based on your own projects, so each module leaves you with something you can use at work the next day. You’ll develop the skills to turn conflicting stakeholder notes into a scoped build plan and build it in small, verifiable stages. You’ll connect projects to GitHub and turn noisy AI commit histories into clear pull request descriptions, changelogs and release notes. You’ll design data models in Lovable Cloud with authentication, row-level security and realistic test data, then run AI-assisted reviews and write test plans that catch weak tests before your users find the bugs. Finally, you’ll set up team workspaces and project knowledge, add automated quality gates, hand projects off to local development, and pitch and plan a pilot that wins support from engineers and leadership. What makes this course unique is its focus on teams rather than solo builders. Instead of just prompting an AI to produce an app, you’ll learn to keep a human in the loop at every step: deciding what the AI may not decide for you, checking each change against clear acceptance criteria, and agreeing on rules that sceptical senior engineers can actually support. This course is designed for tech leads, engineering managers and developers who want to adopt AI-augmented development with confidence. It’s also a good fit for product-minded builders who want their Lovable prototypes to survive contact with a real team and real production.

Syllabus

How to Build and Publish a Working Web App Prototype with Lovable
- Wondering how to get from a rough idea to an app your team can actually click through today? This module shows you how. You will turn a short project brief into a working web app with Lovable, refine its interface with point-and-click visual edits and targeted prompts, and publish it to a public URL anyone on your team can open. You'll use a plain-language explanation of what an AI app builder generates and which decisions still need a human, an AI assistant to convert scattered stakeholder notes into a build-ready brief with staged prompts, and Lovable's Agent mode, Chat mode, Visual Edits, and Publish panel. This module is for tech leads, engineering managers, developers, and product-minded builders who want a fast, reliable way to prototype; no prior Lovable experience is assumed. By the end, you will have a live, shareable app built from your own brief and a reusable brief template you can apply to the next idea your team wants to test.
How to Sync a Lovable Project with GitHub for Team Version Control
- Wondering how to keep AI-generated changes and your developers' commits in one place without anyone overwriting anyone else's work? This module answers exactly that. You will connect a Lovable project to a GitHub repository with two-way sync, push a human edit from GitHub back into Lovable, and run a full branch, pull request, and merge cycle on the project. You'll use a plain-language explanation of how Lovable turns every AI change into a commit and what breaks sync, the Lovable GitHub App, the GitHub web interface, and an AI assistant that turns long lists of terse auto-generated commits into readable PR descriptions and changelogs. This module is for tech leads and developers bringing Lovable into a team that already works in Git; basic familiarity with GitHub helps, but no prior Lovable integration experience is required. By the end, you will have a version-controlled Lovable project with a merged, human-authored pull request and a changelog prompt template your team can reuse on every release.
How to Add Authentication and a Secure Database with Lovable Cloud
- Wondering whether the login and database Lovable just generated would actually stop one user from seeing another user's data? This module shows you how to find out and fix it. You will enable Lovable Cloud on an app, add email sign-up and login, create a user-owned data table protected by row-level security, and run the built-in security scan until it comes back with no critical findings. You'll use a plain-language walkthrough of authentication, data ownership, and row-level security, an AI assistant to design the data model and generate realistic seed data before you prompt, and Lovable Cloud's database view and security scan. This module is for tech leads and builders who need AI-built apps to handle real user data safely; no database administration or security background is assumed, and every term is explained before it's used. By the end, you will have an app with working authentication, isolated per-user data verified with two test accounts, and a clean security scan you can show your team.
How to Review and Test AI-Generated Code Before It Reaches Production
- Wondering how much you can trust a pull request that an AI wrote in thirty seconds? This module gives you a repeatable way to decide. You will review a Lovable-generated pull request using a risk-based review tier, run an AI-assisted code review, fix the issues it surfaces, and add automated tests that cover the changed feature before merging. You'll use a clear breakdown of the most common failure patterns in AI-generated code, a three-tier review model that matches review depth to risk, an AI code reviewer in GitHub, Lovable's code editor with explain-first prompts, and an AI assistant to generate unit and end-to-end tests from user stories. This module is for tech leads, reviewers, and developers responsible for what reaches production; you should be comfortable reading basic JavaScript or TypeScript, but no testing framework experience is required. By the end, you will have merged a reviewed, repaired pull request with at least one new test and a review checklist your team can apply to every AI-generated change.
How to Set Up Shared Conventions So Lovable Generates Consistent Team Code
- Wondering why two teammates give Lovable the same request and get back completely different code? This module shows you how to stop that drift. You will write project knowledge that sets your stack, component style, and naming rules, set up a shared workspace with members and roles, and build a team prompt playbook, then defend those conventions in a conversation with a skeptical senior engineer. You'll use a plain-language introduction to context engineering, a side-by-side comparison of output with and without project knowledge, Lovable's workspace and remix settings, and an AI assistant that turns your best past prompts into reusable templates and a definition of done. This module is for tech leads and engineering managers standardizing how a team works with AI builders; no prior experience with prompt libraries or team AI policies is required. By the end, you will have a written project knowledge file, a team prompt playbook, and a senior engineer's agreement to adopt three concrete conventions.
How to Plan a Lovable Rollout Across a Development Team
- Wondering how to move Lovable from one enthusiastic developer's side project to a sanctioned part of your team's workflow? This module walks you through exactly that. You will define who prototypes and who hardens in a human-plus-AI team, add an automated quality gate to a Lovable repository, hand an app off to a developer's local environment, and assemble a pilot rollout plan you then pitch to a cautious CTO. You'll use a clear overview of operating models, governance, and cost control for AI-assisted teams, GitHub Actions for lint, build, and test checks, VS Code for local handoff, and an AI assistant to draft a pilot plan with baseline metrics, a risk register, and exit criteria. This module is for tech leads and engineering managers who need leadership buy-in before scaling a new tool; no prior experience writing rollout or change-management plans is assumed. By the end, you will have a pilot plan with success metrics and quality gates and a CTO's conditional approval to run it.

Taught by

Anton Voroniuk

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