Training course · Prompt Engineering · Marketing

Prompt Engineering for Marketing Teams

Vendor-neutral prompt engineering: roles, context, few-shot examples, prompt chains, prompt libraries and systematic quality checks.

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

“Prompt Engineering for Marketing Teams” is a hands-on course for roles such as Marketing managers and heads of marketing, Performance and digital marketing specialists and Product and brand managers. Rather than learning Prompt Engineering in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Marketing.

Whether you use ChatGPT, Claude, Copilot, Gemini or Le Chat, the quality of the output depends largely on how the task is framed. Prompt engineering is the skill of conveying goal, context, constraints and desired format to a language model so that it reliably produces usable results. In this vendor-neutral practical day you learn proven techniques that work across all mainstream AI assistants: defining roles and audiences, selecting relevant context, working with examples and breaking complex tasks down into traceable steps. You try out every technique directly on tasks from your own working day.

The second part focuses on repeatability and quality across the team. You develop prompt templates with placeholders, build multi-step prompt chains for analysis, writing and review processes, and set up a shared prompt library. You learn to evaluate output systematically – with review criteria, test cases and targeted self-checks by the model – and to avoid typical pitfalls such as hallucinations, ambiguity and missing context. By the end you have a structured approach that works regardless of the tool you use and continues to pay off as new models arrive.

Marketing teams now cover more channels, audiences and formats than ever, often on the same budget. Generative AI is fundamentally changing the work: AI assistants support market research, persona development and positioning, draft campaign concepts, landing page copy and ad variants and analyse campaign data. AI agents and automation take over routines such as adapting content for different channels, reporting or enriching leads. The value does not come from mass production but from faster testing, better personalisation and more time for strategy and creativity. Clear brand guidelines as the foundation for prompts, careful handling of customer data and editorial review keep content accurate, legally sound and on brand.

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?

  • Marketing managers and heads of marketing
  • Performance and digital marketing specialists
  • Product and brand managers
  • Marketing operations and CRM owners
  • Agency staff

Typical challenges we address

  • Delivering a consistent brand message across many channels and formats
  • Testing campaigns quickly and drawing meaningful conclusions
  • Sharpening target groups and personas with data
  • Organising content production and approvals with limited resources

What you will be able to do

  • You understand how language models process prompts and derive effective wording from this.
  • You structure prompts with role, context, task, constraints and output format.
  • You use few-shot examples and prompt chains for complex, multi-step tasks.
  • You create reusable templates and build a shared prompt library.
  • Use AI for audience analysis, campaign concepts and copy variants within your brand guidelines.
  • Analyse campaign data with AI support and make well-founded decisions.
  • Automate marketing routines in a privacy-compliant way with clear approval steps.

Course outline

Module 1 How language models process prompts
  • Core principles of language models: probabilities, context window, limitations
  • Why wording, order and examples change the result
  • What causes hallucinations and how prompts reduce them
  • Differences and common ground between mainstream AI assistants
Module 2 Building blocks of an effective prompt
  • Defining role, audience, task, context and output format
  • Stating constraints, exclusions and quality criteria
  • Structured input with sections, delimiters and tags
  • Asking the model to raise questions instead of making assumptions
Module 3 Advanced techniques
  • Zero-shot, one-shot and few-shot: setting style and structure with examples
  • Requesting step-by-step reasoning for complex tasks
  • Prompt chains: separating research, drafting, review and revision
  • Using change of perspective, counter-arguments and model self-critique
Module 4 Templates and prompt libraries
  • Creating reusable templates with placeholders and variables
  • Storing persistent instructions in projects, custom assistants and agents
  • Building, naming, maintaining and sharing a team prompt library
  • Documenting templates: purpose, inputs, expected output, owner
Module 5 Quality assurance and responsible use
  • Defining review criteria and scoring rubrics for AI output
  • Setting up test cases and systematically comparing and improving prompts
  • Verifying facts, figures and sources; establishing the four-eyes principle
  • Data protection, confidential content and transparency under GDPR and the EU AI Act
Module 6 Practice lab: Marketing
  • Audiences and personas
  • Campaign concepts
  • Ad and copy variants
  • Campaign reporting analysis
  • Competitor monitoring
  • Marketing automation
Module 7 Data protection, quality & transfer to daily work
  • Marketing use of AI must comply with the GDPR and ePrivacy rules on tracking, consent and personalisation; customer data belongs only in approved tools. Unfair competition law applies to advertising claims, copyright and trademark law to generated content, and the EU AI Act's transparency duties to AI-generated media.
  • 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

Audiences and personas

An AI assistant condenses market studies, customer feedback and sales notes into personas with needs, objections and buying criteria, then derives key messages and value propositions for campaigns.

Campaign concepts

From the brief, goals and budget, AI develops several campaign ideas with a central idea, channel mix, messages and KPIs, which the team evaluates, combines and refines.

Ad and copy variants

AI creates variants of headlines, ad copy, subject lines and calls to action within brand guidelines and prepares A/B tests whose results are then analysed.

Campaign reporting analysis

Exports from ad platforms, web analytics and CRM are combined, anomalies explained and recommendations drafted, so reporting meetings can focus on decisions rather than data gathering.

Competitor monitoring

AI summarises publicly available information on competitors, such as website messaging, product launches and pricing communication, and highlights differences from your own positioning.

Marketing automation

Workflows enrich new leads, segment contacts, draft personalised nurture emails and notify sales as soon as a lead meets defined qualification criteria.

Prerequisites

Some initial experience with at least one AI assistant such as ChatGPT, Claude, Copilot or Gemini is helpful; no technical background is required. You work in marketing or at an agency and understand the basics of campaign planning and digital channels.

Technical requirements

A laptop with an up-to-date browser and access to at least one AI assistant, ideally the tool approved within your organisation.

Data protection & compliance

Marketing use of AI must comply with the GDPR and ePrivacy rules on tracking, consent and personalisation; customer data belongs only in approved tools. Unfair competition law applies to advertising claims, copyright and trademark law to generated content, and the EU AI Act's transparency duties to AI-generated media.

Suggested dates

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

  • Thu, 26 November 2026 Online Request
  • Wed, 2 December 2026 Kaiserslautern Request
  • Fri, 11 December 2026 Online Request
  • Mon, 14 December 2026 Bremen Request
  • Wed, 20 January 2027 Salzburg Request
  • Tue, 2 February 2027 Online Request

Frequently asked questions

Do these techniques work with every AI assistant?

Yes, the course is deliberately vendor-neutral. The principles – a clear task, relevant context, examples, structured output and review steps – work equally well in ChatGPT, Claude, Copilot, Gemini, Le Chat and other tools. Where assistants differ in detail, for example with projects or custom assistants, we show the corresponding features in each.

Won't better models soon make prompt engineering obsolete?

Models are getting better at interpreting vague requests, but they still cannot know what you do not tell them: audience, quality standards, internal requirements or relevant background. Good prompts are therefore mainly good task descriptions. Templates, prompt chains and quality checks also keep their value for teams as new models arrive.

What is a prompt library and how do we maintain it as a team?

A prompt library is an organised collection of proven templates for recurring tasks, each with its purpose, required inputs, a sample result and an owner. It can live in a shared document, a wiki or directly in the AI tool's projects. In the course you set up a first library and agree simple rules for maintenance and approval.

How do we keep AI-generated content on brand?

Start with well-documented brand guidelines covering tone of voice, sample copy and banned terms, and provide them as context to AI assistants. Combined with reusable prompt templates and editorial approval, this produces consistent copy. In the course we build such templates together for your channels.

Who owns the rights to AI-generated content?

Purely machine-generated content is generally not protected by copyright in many jurisdictions, including Germany, whereas works substantially shaped by people can be. You also need to check that outputs do not infringe third-party rights such as trademarks or well-known imagery. We discuss practical rules for approval and labelling.

How much does “Prompt Engineering for Marketing Teams” 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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