Training course – Generative AI – Media & Journalism

Generative AI for Media and Journalism

Vendor-neutral, hands-on day: how language models work, where they go wrong and how to roll out AI successfully in your team.

Duration
6 hours, one session
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Generative AI for Media and Journalism” is designed for roles such as Reporters and editors in print, online and broadcast, News desk chiefs and section editors and Social media and audience teams and shows step by step how to use Generative AI productively, safely and transparently in Media & Journalism – from the fundamentals to advanced workflows.

Generative AI has reached most organisations, yet teams often lack a shared understanding of what language models can actually do and where their limits lie. In this vendor-neutral course you will learn how large language models generate text, why they hallucinate and how careful prompting, context and source checking lead to reliable results. Rather than relying on any provider's marketing claims, you will work with transparent criteria that help you compare tools and judge their fit for your own tasks.

The second focus is everyday implementation. You will identify concrete use cases in your area of work, weigh benefit, risk and effort, and draft a realistic roll-out plan for your team. This covers ground rules for handling confidential data, a practical prompt library and ways to build acceptance and make results measurable. Throughout the day you work with examples from your own field, so you leave with an actionable roadmap rather than a set of abstract ideas.

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 can explain clearly how language models generate text and why hallucinations occur.
  • You can write prompts that consistently deliver useful, verifiable results.
  • You can compare AI tools against transparent criteria covering data protection, features and cost.
  • You can identify and prioritise concrete use cases for your area of work.
  • 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 How large language models work
  • Tokens, context windows and probabilities: what happens when a model generates text
  • Training knowledge versus web search versus your own documents (RAG)
  • Text, images, audio and code: what multimodal models can do today
  • Realistically assessing typical strengths and weaknesses in office work
Module 2 Spotting hallucinations and getting reliable output
  • Why models produce plausible-sounding errors and when the risk increases
  • Prompting techniques: role, context, examples, output format and step-by-step instructions
  • Requesting sources, cross-checking facts and introducing a four-eyes principle
  • Scoping tasks so that AI assists rather than decides
Module 3 Choosing tools without vendor bias
  • Overview of chat assistants, AI built into office suites and specialist tools
  • Criteria: data processing, hosting location, admin features, integrations and pricing model
  • Enterprise licences, API access and locally hosted open-source models compared
  • Building a simple scoring matrix for your own tool selection
Module 4 Identifying use cases in your field
  • Systematically capturing recurring writing, research and analysis tasks
  • Rating benefit, risk and implementation effort for each use case
  • Distinguishing quick wins from strategic initiatives
  • Creating a prioritised use-case backlog for your team
Module 5 Rolling out generative AI in your team
  • Usage rules: which data may go into which tool and who signs off results
  • Building and maintaining a shared prompt library and templates
  • AI champions, training formats and peer exchanges to embed new habits
  • Measuring success: documenting time saved, quality and user satisfaction
Module 6 Practice lab: Media & Journalism
  • Document research
  • Transcribing and analysing interviews
  • Headlines and teasers
  • Adapting formats
  • Unlocking the archive
  • Supporting fact-checking
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.

Prerequisites

No technical background is required. It helps to bring along typical tasks from your day-to-day work that you would like to support with AI. Professional experience in journalism or editorial work; no technical background required.

Technical requirements

You will need a laptop with an up-to-date browser and internet access. A free account with a common AI assistant is sufficient; company licences are welcome.

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.

Dates & locations

For every location we suggest a date roughly four weeks ahead. Your preferred date is always possible.

  • Munich Thu, 10 December 2026 6 hrs Request
  • Salzburg Wed, 16 December 2026 6 hrs Request
  • Berlin Thu, 10 December 2026 6 hrs Request
  • Augsburg Fri, 11 December 2026 6 hrs Request
  • Hanover Wed, 16 December 2026 6 hrs Request
  • Bremen Mon, 14 December 2026 6 hrs Request
  • Stuttgart Fri, 11 December 2026 6 hrs Request
  • Kaiserslautern Fri, 11 December 2026 6 hrs Request
  • Online Wed, 16 December 2026 6 hrs Request

Frequently asked questions

Is the course tied to a specific AI tool?

No. The training is deliberately vendor-neutral. You work with widely used chat assistants and learn principles that apply to all major language models. That way you can transfer your skills to whichever tool your organisation has approved and evaluate new offerings yourself using clear criteria instead of relying on marketing statements.

How is confidential company data handled during the course?

For the exercises you use anonymised or fictitious examples from your area of work. At the same time you learn which categories of data may be entered into which tools, what to look for regarding data processing agreements and use of inputs for training, and how to set out these rules clearly for your team.

Will we leave with something concrete to implement?

Yes. You leave with a prioritised use-case backlog, a scoring matrix for tool selection, tested prompt templates and a draft set of team usage rules. These materials are designed so you can take them straight into internal discussions with management, IT and your data protection officer.

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 “Generative AI for Media and Journalism” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 6 hours of live training with a trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. For every location we suggest a date roughly four weeks ahead. In the request form you can always describe your preferred date or period in free text – we check availability and get back to you 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 and Kaiserslautern. We can also run the course exclusively for your team.

Can the course be tailored to our team?

Yes. Tailor-made courses cost approx. €4,500 more, depending on the topic and by arrangement. We agree content, examples and exercises with you in advance.

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