Training course · Lovable · Media & Journalism

Lovable for Media and Journalism

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

“Lovable for Media and Journalism” is a hands-on course for roles such as Reporters and editors in print, online and broadcast, News desk chiefs and section editors and Social media and audience teams. Rather than learning Lovable in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Media & Journalism.

Lovable lets you build web applications simply by describing what you need, with no programming knowledge required. In this course, business users from marketing, sales, HR, operations or product management learn how to turn an idea into a clickable, working application within hours: a landing page for a campaign, an internal tool for logging requests, a simple customer portal or a prototype that helps you pitch an idea convincingly inside your organisation.

The course is hands-on throughout. You work on a small project of your own and learn to state requirements clearly, store data securely, set up logins and publish the result. Just as important is a realistic perspective: which projects you can deliver without IT support, when to involve IT or your data protection officer, and how to hand a successful prototype over to a development team. That way you use Lovable productively without creating shadow IT or security risks.

Newsrooms, publishers, broadcasters and content teams are caught between economic pressure and the demands of quality journalism. Generative AI is fundamentally changing research, production and distribution. It helps reporters work through documents and datasets faster, transcribe and summarise interviews, develop headline and teaser variants, adapt stories for different channels and make archives searchable. AI agents can sort wire copy or handle monitoring tasks. At the same time, AI raises core questions: how do you safeguard accuracy, how do you make AI use transparent to your audience and how do you respect copyright? Media professionals therefore need competence in both productive use and critical verification.

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?

  • Reporters and editors in print, online and broadcast
  • News desk chiefs and section editors
  • Social media and audience teams
  • Freelance journalists
  • Content and corporate publishing teams

Typical challenges we address

  • Intense time pressure alongside a growing number of channels and formats
  • Large volumes of documents, data and wire material
  • Safeguarding accuracy and credibility
  • Unresolved questions about copyright and labelling of AI content

What you will be able to do

  • You describe requirements in a way that lets Lovable generate a usable web application.
  • You create a landing page with a form and publish it on your own.
  • You build an internal tool with login, data storage and roles without any coding.
  • You develop clickable prototypes and use them to align with stakeholders.
  • Use AI for research, transcription and production efficiently and faithfully to sources.
  • Check AI output systematically for accuracy and avoid typical pitfalls.
  • Apply rules on copyright, labelling and press ethics confidently when using AI.

Course outline

Module 1 From idea to first draft
  • What Lovable can do and what happens behind the scenes, explained plainly
  • Breaking an idea down into goal, user groups, pages and content
  • Writing your first prompt and judging the result
  • Making small, precise change requests instead of starting from scratch
Module 2 Landing pages and web presence
  • Campaign and event pages with a clear message and call to action
  • Adding images, copy and your corporate design
  • Contact and registration forms that store submissions
  • Checking how pages look on mobile and desktop
Module 3 Internal tools and forms
  • Building input forms, overview lists and simple dashboards
  • Storing data in a database without writing SQL
  • Setting up logins and roles: who may see and change what
  • Spotting typical errors and fixing them with the undo history
Module 4 Customer portals and prototypes
  • Simple portals for customers or partners with a protected area
  • Clickable prototypes for product ideas and stakeholder presentations
  • Collecting feedback and implementing iterations in a focused way
  • Handing a well-documented prototype over to development or IT
Module 5 Publishing, security and ground rules
  • Publishing your app and making it available on your own domain
  • Personal data, GDPR and coordination with IT and data protection
  • Costs, usage limits and realistic effort
  • Recognising when a project belongs in professional developers’ hands
Module 6 Hands-on project: your own solution for Media & Journalism
  • Quote database – A small app that stores quotes from interviews with source, date and approval status, makes them searchable and exports them for stories.
  • Story planning board – An internal tool for editorial meetings that manages story ideas, assignments, channels and publication dates in one clear overview. Status is visible to everyone.
  • Teaser workshop – A web app that generates headline and teaser variants per channel for a pasted article and automatically checks character limits.
Module 7 Data protection, quality & transfer to daily work
  • Press codes and journalistic due diligence apply unchanged: AI output must be verified and AI use made transparent to the audience in line with in-house guidelines. Copyright and related rights in source material and generated content must be respected, and source protection rules out entering confidential source information into unapproved tools.
  • 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

Document research

Lengthy reports, court rulings, budgets or leaked datasets are searched and summarised with AI and checked for relevant passages – every statement is then verified against the source.

Transcribing and analysing interviews

Audio and video interviews are transcribed, key statements and quotes are highlighted and sorted by topic, so stories can be written faster and quotes attributed precisely.

Headlines and teasers

For a finished story, AI generates variants for headlines, standfirsts, social posts and push alerts that match the brand's tone and from which the editors choose.

Adapting formats

An article is turned into newsletter summaries, explainers, podcast scripts or easy-read versions without changing what it says. Editors check every version for accuracy and house style before publication.

Unlocking the archive

Archive material is tagged, summarised and made searchable so that background, timelines and earlier coverage can be retrieved quickly. Dossiers on people, places and topics stay up to date with little effort.

Supporting fact-checking

AI helps identify checkable claims in texts, formulate verification questions and point out contradictions; the verification itself is done with primary sources. Due diligence becomes more systematic.

Possible hands-on projects

Quote database

A small app that stores quotes from interviews with source, date and approval status, makes them searchable and exports them for stories.

Story planning board

An internal tool for editorial meetings that manages story ideas, assignments, channels and publication dates in one clear overview. Status is visible to everyone.

Teaser workshop

A web app that generates headline and teaser variants per channel for a pasted article and automatically checks character limits.

Prerequisites

Confident use of a web browser and standard office software is all you need. No programming knowledge is required. Professional experience in journalism or editorial work; no technical background required.

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

Press codes and journalistic due diligence apply unchanged: AI output must be verified and AI use made transparent to the audience in line with in-house guidelines. Copyright and related rights in source material and generated content must be respected, and source protection rules out entering confidential source information into unapproved tools.

Suggested dates

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

  • Fri, 27 November 2026 Online Request
  • Fri, 4 December 2026 Hanover Request
  • Mon, 14 December 2026 Online Request
  • Tue, 12 January 2027 Munich Request
  • Fri, 22 January 2027 Online Request
  • Thu, 4 February 2027 Stuttgart 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.

Does AI-generated content need to be labelled?

That is determined by legislation, voluntary commitments and your organisation's guidelines. The EU AI Act sets transparency obligations for certain AI-generated content, and many newsrooms go further with their own policies. In the course we discuss when labelling is required and how to document editorial responsibility.

Can I use AI for fact-checking?

AI helps identify checkable claims and plan verification steps, but it is not a reliable source in itself. Language models can state false facts plausibly. Verification always relies on primary sources, documents and conversations. The course demonstrates a workflow that uses AI as an assistant without compromising rigour.

How much does “Lovable for Media and Journalism” 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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