Training course · Generative AI · Product Management

Generative AI for Product Managers

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

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 Generative AI with the everyday processes of Product Management. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

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.

Product managers connect customer needs, business goals and technical delivery. Their day revolves around discovery, prioritisation, specification and alignment with engineering, design, sales and leadership. Generative AI speeds up almost every one of these steps: it analyses customer feedback, interviews and support tickets, helps write product requirements and user stories, structures competitor analyses and prepares roadmap communication for different stakeholders. With vibe coding, product managers can build clickable prototypes themselves and test ideas before committing engineering capacity. AI agents bring together signals from analytics, CRM and feedback tools. That leaves more time for product vision, decisions and conversations with customers.

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?

  • Product managers
  • Product Owners in agile teams
  • Heads of Product and product directors
  • Product marketing teams
  • UX researchers and product designers

Typical challenges we address

  • Large amounts of unstructured feedback from many channels
  • Heavy effort for specifications and stakeholder alignment
  • Prioritising under uncertainty and competing interests
  • Long lead times from idea to testable prototype

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.
  • Synthesise customer feedback and market information with AI into well-founded product decisions.
  • Produce specifications, user stories and roadmap communication faster and more precisely with AI.
  • Build clickable prototypes through vibe coding to validate ideas early with users.

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: Product Management
  • Synthesising customer feedback
  • PRDs and user stories
  • Preparing prioritisation
  • Competitor and market analysis
  • Prototypes via vibe coding
  • Roadmap and release communication
Module 7 Data protection, quality & transfer to daily work
  • Customer feedback and usage data often contain personal information and are subject to the GDPR; they should be pseudonymised before being processed in AI tools. Roadmaps and unreleased product information are trade secrets, and AI features in your own product must be assessed against the requirements of the EU AI Act.
  • 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

Synthesising customer feedback

Interviews, support tickets, app reviews and surveys are grouped by theme, problem and request and turned into an opportunity overview with supporting quotes for discovery.

PRDs and user stories

Problem statement, target users and goals become drafts of product requirement documents, user stories with acceptance criteria and edge cases, refined with engineering and design.

Preparing prioritisation

AI helps score initiatives using frameworks such as RICE or Kano, surface assumptions and prepare arguments for and against options ahead of prioritisation discussions. Disagreements become easier to resolve.

Competitor and market analysis

Feature comparisons, positioning and pricing models of competitors are structured and updated regularly; claims are backed by sources and checked critically. You keep track of market moves without lengthy manual research.

Prototypes via vibe coding

Product managers use AI coding tools to build clickable prototypes and simple functional demos to test concepts with users and make requirements tangible for the team.

Roadmap and release communication

Roadmaps, release notes, internal enablement material and customer communication are tailored to the audience – technical for engineering, benefit-led for sales and customers. Every audience gets exactly what it needs.

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. Experience in product management or as a Product Owner; no programming skills 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

Customer feedback and usage data often contain personal information and are subject to the GDPR; they should be pseudonymised before being processed in AI tools. Roadmaps and unreleased product information are trade secrets, and AI features in your own product must be assessed against the requirements of the EU AI Act.

Suggested dates

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

  • Tue, 24 November 2026 Online Request
  • Mon, 30 November 2026 Augsburg Request
  • Wed, 16 December 2026 Online Request
  • Fri, 15 January 2027 Kaiserslautern Request
  • Fri, 22 January 2027 Online Request
  • Tue, 2 February 2027 Bremen 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.

Can AI make product decisions?

No, but it improves the basis for them. AI condenses feedback, surfaces assumptions and prepares options. Product vision, weighing trade-offs and accountability for decisions remain with product management. In the course you use AI deliberately as an analysis and sparring partner without giving up critical judgement.

How far can I get with vibe-coded prototypes?

Very far for clickable interfaces, simple logic and user testing – often within a few hours. Production-ready software with security, scalability and maintainability remains engineering's job. The course shows how prototypes improve collaboration with the team rather than replacing it.

How much does “Generative AI for Product Managers” 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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