Training course · Prompt Engineering · Project Management

Prompt Engineering for Project 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

This course connects Prompt Engineering with the everyday processes of Project Management. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

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.

Project management is largely information work: clarifying requirements, building plans, assessing risks, reporting status and keeping a wide range of stakeholders aligned. Generative AI speeds up exactly these activities. It turns meeting notes into minutes and action lists, structures project charters and work breakdown structures, writes status reports for different audiences and supports risk workshops with well-grounded suggestions. AI agents connect project management tools, inbox and calendar and send automatic reminders about overdue tasks. Whether you work waterfall, agile with Scrum or hybrid, project leads gain time for what AI cannot do: making decisions, resolving conflicts and leading the team.

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?

  • Project managers
  • PMO staff and portfolio managers
  • Scrum Masters and agile coaches
  • Workstream and work package leads
  • Business teams with occasional project responsibility

Typical challenges we address

  • Heavy documentation workload for minutes, reports and plans
  • Many stakeholders with different information needs
  • Risks and dependencies spotted too late
  • Information scattered across tools, emails and chats

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.
  • Produce project documents such as charters, plans, minutes and reports faster and more consistently with AI.
  • Use AI as a sparring partner for risk analysis, planning and stakeholder communication.
  • Design simple automations for follow-up and reporting in your project environment.

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: Project Management
  • Meeting minutes and actions
  • Project charter and WBS
  • Audience-specific status reports
  • Identifying risks
  • Agile artefacts
  • Automated follow-up
Module 7 Data protection, quality & transfer to daily work
  • Project documents often contain confidential client data, contract details and personal data about team members, which may only be processed in approved AI environments. Analysing employee performance or workload data requires compliance with the GDPR and, where applicable, works council co-determination rights.
  • 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

Meeting minutes and actions

Notes or transcripts become structured minutes with decisions, open points and actions including owners and due dates, ready to be transferred into the project tool.

Project charter and WBS

AI helps turn a project idea into a charter with objectives, scope boundaries and success criteria, plus an initial work breakdown structure with work packages and milestones.

Audience-specific status reports

From raw data such as task progress, budget and risks, AI drafts status reports at different levels of detail – concise for the steering committee, detailed for the core team.

Identifying risks

In risk workshops AI suggests typical risks for the project type and industry, helps assess likelihood and impact and drafts mitigation actions. Owners and review dates are assigned straight away.

Agile artefacts

User stories with acceptance criteria, backlog refinement, sprint goals and retrospective formats are prepared with AI and then sharpened together by the team. Definitions of done can be drafted too.

Automated follow-up

AI agents check task progress regularly, remind owners about overdue items, summarise the week's changes and prepare a short project newsletter from them. The team stays informed without extra effort.

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 leading or contributing to projects, whether traditional, agile or hybrid.

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

Project documents often contain confidential client data, contract details and personal data about team members, which may only be processed in approved AI environments. Analysing employee performance or workload data requires compliance with the GDPR and, where applicable, works council co-determination rights.

Suggested dates

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

  • Wed, 25 November 2026 Online Request
  • Fri, 11 December 2026 Berlin Request
  • Mon, 14 December 2026 Online Request
  • Wed, 13 January 2027 Stuttgart Request
  • Tue, 26 January 2027 Online Request
  • Thu, 11 February 2027 Hanover 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 create a project plan?

AI provides a solid first draft of the work breakdown structure, milestones and dependencies if you describe objectives and constraints clearly. Effort estimates, resources and priorities still need to be agreed with the team. The course shows how to review AI drafts systematically and transfer them into your project tool.

Does this work in agile projects too?

Yes. In Scrum or Kanban, AI helps write user stories and acceptance criteria, prepare refinements and retrospectives and summarise sprint results for stakeholders. Decisions about the backlog and delivery naturally remain with the team and the Product Owner. AI supports, the team decides.

How much does “Prompt Engineering for Project 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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