Training course · Lovable · API Development

Lovable for API Developers

Build web apps from prompts: take Lovable from idea to a working React app with Supabase backend, GitHub sync and deployment.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects Lovable with the everyday processes of API Development. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Lovable turns plain-language descriptions into complete web applications built on React, TypeScript, Tailwind CSS and Vite. In this one-day course you learn how to phrase requirements so that Lovable produces clean, maintainable code, how to connect a database, authentication and storage through the Supabase integration, and how to steer changes iteratively instead of starting over every time something breaks. The focus is on real workflows: from the first prompt through data model and permission design to a published application running on your own domain.

For developers, Lovable becomes genuinely useful when the generated code does not stay locked inside the tool. You therefore work extensively with the two-way GitHub sync, review the generated code in your own editor, add tests and move the project into your existing toolchain where needed. You also get a clear view of the limits: Row Level Security, Edge Functions, secrets handling, performance and the maintainability of larger codebases. That lets you judge when Lovable is the fastest route and when conventional development or an agentic coding tool is the better fit.

APIs form the backbone of modern software landscapes and are increasingly the interface through which AI agents interact with systems. AI coding assistants speed up the entire API lifecycle, from drafting an OpenAPI or AsyncAPI specification to generating server stubs, clients and contract tests, through to documentation and versioning. At the same time, demands for consistency, security and backward compatibility keep rising, because APIs are consumed by many teams, partners and automated agents. In this course you learn to practise API-first development with AI support, enforce design guidelines automatically, catch breaking changes early and expose existing interfaces to AI agents through the Model Context Protocol or tool definitions.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Backend developers building REST, GraphQL or gRPC interfaces
  • API architects and owners of API governance
  • Integration developers connecting partners and platforms
  • Platform engineering teams running API gateways and developer portals
  • Developers making company data available to AI agents

Typical challenges we address

  • Inconsistent API design across teams with differing conventions for errors, pagination and versioning
  • Specification, implementation and documentation drift apart
  • Breaking changes are only discovered by consumers
  • Securing authentication, authorisation and rate limiting as users and agent traffic grow

What you will be able to do

  • You write prompts that lead Lovable to produce well-structured, maintainable React applications.
  • You connect Supabase for database, authentication and storage and verify Row Level Security.
  • You iterate on the design deliberately without breaking parts of the app that already work.
  • You sync projects with GitHub and continue development in your usual coding environment.
  • Practise API-first development with AI support, from specification to clients, tests and documentation.
  • Automate design governance, contract testing and breaking-change detection in your pipeline.
  • Make existing APIs available to AI agents securely and in a controlled way, for example via the Model Context Protocol.

Course outline

Module 1 Fundamentals and prompting strategy
  • How Lovable works: the generated stack of React, TypeScript, Tailwind CSS and shadcn/ui
  • Describing requirements in a structured way: audience, pages, data objects, roles
  • Chat mode versus direct edits: when to plan and when to build
  • Using the knowledge base and project instructions for consistent output
Module 2 Backend with Supabase
  • Connecting a Supabase project and deriving tables from your prompt
  • Setting up authentication with email, magic links and OAuth providers
  • Understanding, reviewing and tightening Row Level Security policies
  • Using Edge Functions, storage buckets and secrets for external APIs
Module 3 Design iteration and UI quality
  • Visual editing of individual elements and targeted layout prompts
  • Working from screenshots and Figma designs
  • Ensuring responsive behaviour, accessibility and design-system consistency
  • Debugging with console output and reverting to earlier versions
Module 4 GitHub sync and a professional workflow
  • Setting up two-way synchronisation with a GitHub repository
  • Continuing work locally in VS Code, Cursor or Claude Code
  • Introducing branches, code review and tests for generated code
  • Keeping dependencies, environment variables and project structure tidy
Module 5 Deployment, operations and limits
  • Publishing with Lovable hosting and a custom domain
  • Alternative deployments via Vercel, Netlify or your own infrastructure
  • Realistically assessing credit usage, project size and common dead ends
  • Security and data-protection review before go-live, including GDPR questions
Module 6 Practice lab: API Development
  • Generating OpenAPI specs
  • Generating servers and clients
  • Building contract tests
  • Detecting breaking changes
  • Reviewing API security
  • Opening APIs to AI agents
Module 7 Data protection, quality & transfer to daily work
  • APIs frequently carry personal and confidential data: you learn to create specifications and sample data without real customer records, keep API keys and tokens out of the AI context and apply data protection by design under the GDPR as well as NIS2 requirements.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Generating OpenAPI specs

Draft complete OpenAPI specifications with schemas, examples, error objects and security schemes from requirements, domain models or existing code, and check them against your style guide rules.

Generating servers and clients

Derive controller scaffolding, validation logic and type-safe client SDKs for several languages from a specification, keeping generated parts cleanly separated from hand-written business logic. Regenerating after spec changes is simple.

Building contract tests

Define consumer-driven contracts, generate test cases for edge values, error paths and authentication, and run them automatically in CI against both the specification and the live implementation.

Detecting breaking changes

Compare two versions of a specification, identify incompatible changes, write migration notes for consumers and plan deprecation strategies using versioning and sunset headers. The check runs on every pull request.

Reviewing API security

Analyse endpoints for typical weaknesses from the OWASP API Security Top 10, such as broken object-level authorisation, mass assignment or missing rate limits, and propose fixes.

Opening APIs to AI agents

Expose existing interfaces as MCP servers or tool definitions, phrase descriptions and parameters so agents use them correctly and scope permissions so agents can only perform approved actions.

Possible hands-on projects

API linter with house rules

A command-line tool that checks OpenAPI files against your design guidelines and posts clear improvement suggestions directly on the pull request.

Mock server from spec

A small service that generates realistic sample responses from a specification so frontend teams can work in parallel with backend development.

MCP server for an internal API

An MCP server that exposes selected endpoints of an internal REST API as tools for AI assistants, with authentication and logging.

Prerequisites

Basic web development knowledge (HTML, CSS, JavaScript) and some experience with Git are helpful. Prior React or Supabase experience is not required. You build backend applications in a mainstream language such as Java, C#, TypeScript, Python or Go and understand HTTP, JSON and REST fundamentals.

Technical requirements

A laptop with an up-to-date browser, a Lovable account (a paid plan is recommended for intensive use) and free GitHub and Supabase accounts.

Data protection & compliance

APIs frequently carry personal and confidential data: you learn to create specifications and sample data without real customer records, keep API keys and tokens out of the AI context and apply data protection by design under the GDPR as well as NIS2 requirements.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Fri, 27 November 2026 Online Request
  • Mon, 7 December 2026 Salzburg Request
  • Thu, 17 December 2026 Online Request
  • Fri, 22 January 2027 Bremen Request
  • Mon, 1 February 2027 Online Request
  • Wed, 17 February 2027 Augsburg Request

Frequently asked questions

Do I own the code Lovable generates?

Yes. The output is standard React and TypeScript code that you can push to your own repository via the GitHub sync. From there it can be developed, tested and deployed entirely independently of Lovable. In the course we show you how to set this up properly from day one so you avoid any lock-in.

Is Lovable suitable for production applications?

For prototypes, MVPs, internal tools and modest customer-facing apps, yes, provided you review security and the data model carefully. Supabase Row Level Security, secrets handling and the maintainability of a growing codebase all need attention. The course covers exactly which checks make sense before you go live.

How does Lovable differ from Cursor or Claude Code?

Lovable is a browser-based app builder with a fixed stack, built-in hosting and Supabase integration, which makes it ideal for getting started quickly. Cursor and Claude Code work inside any existing codebase and technology. Many teams combine them: start in Lovable, then continue in the editor via GitHub.

Is spec-first still worth it when AI writes code so quickly?

More than ever. A precise specification is the best context you can give an AI assistant: it pins down data structures, error cases and security requirements, serves as a contract for consumers and underpins your tests. Without one, AI quickly produces inconsistent endpoints. In the course you use the spec as the single source for generation and validation.

What is the difference between a REST API and an MCP server?

A REST API is aimed at developers and applications, whereas an MCP server exposes capabilities in a standardised format for AI assistants and agents, including descriptions the model can interpret. An MCP server is often a thin layer on top of existing APIs. You learn when that step pays off and how to implement it securely.

How much does “Lovable for API Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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