Training course · Prompt Engineering · Manufacturing

Prompt Engineering for Manufacturing 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 Manufacturing Teams” is a hands-on course for roles such as Production and plant managers, Shift leaders and supervisors and Manufacturing and process engineers. Rather than learning Prompt Engineering in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Manufacturing.

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.

Manufacturers face cost pressure, skills shortages, complex product variants and high expectations for on-time delivery. Generative AI adds a new layer to existing Industry 4.0 solutions: AI assistants make knowledge from manuals, fault logs and work instructions accessible, support shift handovers, summarise KPIs from MES and ERP exports and help with root cause analysis using methods such as 5 Whys or Ishikawa. AI agents and automation create shift reports, analyse machine breakdowns and prepare maintenance orders. Experienced staff in particular benefit when their knowledge is documented and made available to new colleagues. Adoption succeeds when it starts with concrete shop-floor problems, involves works councils and health and safety early and keeps production data secure.

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?

  • Production and plant managers
  • Shift leaders and supervisors
  • Manufacturing and process engineers
  • Maintenance teams
  • Lean and operational excellence leads

Typical challenges we address

  • Capturing and passing on the know-how of experienced staff
  • Analysing breakdowns quickly and reducing downtime
  • Presenting KPIs such as OEE from various systems in an understandable way
  • Keeping work instructions current and clear across many variants

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 knowledge management, root cause analysis and shift reporting in production.
  • Analyse production KPIs with AI and derive improvement actions.
  • Take data security, co-determination and occupational safety into account in manufacturing AI projects.

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: Manufacturing
  • Shop-floor knowledge assistant
  • Root cause analysis
  • Shift reports and handovers
  • Work instructions
  • KPI analysis
  • Maintenance preparation
Module 7 Data protection, quality & transfer to daily work
  • Production data and engineering know-how are trade secrets and belong only in approved AI environments; in Germany, AI-based evaluation of employee performance or behaviour requires works council co-determination. AI components in safety-related machine functions fall under the EU Machinery Regulation and may be high-risk under 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

Shop-floor knowledge assistant

An AI assistant answers questions about machines, changeovers and fault patterns based on manuals, breakdown history and work instructions, always citing the source for verification.

Root cause analysis

AI structures breakdowns and quality issues using 5 Whys, Ishikawa or 8D, suggests possible causes from similar cases and helps document actions and responsibilities.

Shift reports and handovers

Notes, machine data and fault reports are turned into structured shift reports with output, breakdowns and open points, making handover to the next shift easier.

Work instructions

AI turns process descriptions, photos and expert interviews into illustrated, multilingual work instructions and checklists in plain language and keeps variants consistent.

KPI analysis

MES and ERP exports are analysed, OEE losses broken down by availability, performance and quality and anomalies explained clearly for shop-floor meetings.

Maintenance preparation

Automations collect fault reports; AI groups recurring failures, drafts maintenance orders and prioritises actions based on frequency and impact, before maintenance planning takes them over.

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 production, production planning or maintenance and know the processes at your plant.

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

Production data and engineering know-how are trade secrets and belong only in approved AI environments; in Germany, AI-based evaluation of employee performance or behaviour requires works council co-determination. AI components in safety-related machine functions fall under the EU Machinery Regulation and may be high-risk under the EU AI Act.

Suggested dates

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

  • Wed, 25 November 2026 Online Request
  • Tue, 8 December 2026 Salzburg Request
  • Mon, 14 December 2026 Online Request
  • Mon, 18 January 2027 Bremen Request
  • Thu, 4 February 2027 Online Request
  • Wed, 17 February 2027 Augsburg 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.

Do we need to connect AI to our machines?

Not necessarily. Many use cases, such as knowledge assistants, work instructions or root cause analysis, work with documents and MES or ERP exports. Connecting directly to machine data is a later step that requires IT and OT security. In the course we start with use cases you can implement quickly.

How do we gain acceptance from the workforce?

By making AI visibly ease work, for example through faster troubleshooting or simpler documentation, rather than being perceived as a monitoring tool. Involve employees and their representatives early, start with pilot areas and clearly define which data will not be used for performance evaluation.

How much does “Prompt Engineering for Manufacturing 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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