Training course · Prompt Engineering · Product Management

Prompt Engineering for Product Managers

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 Product Managers” is a hands-on course for roles such as Product managers, Product Owners in agile teams and Heads of Product and product directors. Rather than learning Prompt Engineering in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Product Management.

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.

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 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.
  • 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 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: 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

Some initial experience with at least one AI assistant such as ChatGPT, Claude, Copilot or Gemini is helpful; no technical background is required. Experience in product management or as a Product Owner; no programming skills required.

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

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
  • Thu, 3 December 2026 Kaiserslautern Request
  • Mon, 7 December 2026 Online Request
  • Thu, 7 January 2027 Online Request
  • Mon, 11 January 2027 Augsburg Request
  • Tue, 26 January 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.

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 “Prompt Engineering 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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