Training course · ChatGPT · Engineering

ChatGPT for Engineers

Use ChatGPT safely and productively at work: projects, custom GPTs, data analysis, Canvas and privacy settings in team workspaces.

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

“ChatGPT for Engineers” is a hands-on course for roles such as Design and development engineers, Project engineers in plant and machine building and Test and validation engineers. Rather than learning ChatGPT in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Engineering.

For many business teams, ChatGPT is the first AI tool they use every day – yet there is a methodical gap between asking the odd question and achieving real productivity gains. In this hands-on day you learn to apply ChatGPT deliberately to recurring work: drafting and editing text, summarising documents, analysing spreadsheets and structuring decision papers. You work with Projects that keep files and instructions together, with Canvas for reworking longer documents, and with data analysis for Excel and CSV files. Every exercise is based on genuine tasks from your own function.

The second focus is scale and responsibility. You build a custom GPT that knows your templates, tone of voice and review rules, and that colleagues across the workspace can use. We also clarify which data belongs in ChatGPT and which does not, how Team and Enterprise workspaces differ from personal accounts, and which settings matter for model training, memory and sharing. You leave with a personal prompt library, a working GPT and clear ground rules for using ChatGPT responsibly across your organisation.

Engineers in design, development, plant engineering and technical planning spend much of their time on research, documentation, coordination and calculations. Generative AI can reduce that share considerably: AI assistants summarise standards and technical literature, structure requirement and design specifications, support requirements analysis, generate calculation scripts in Python or spreadsheets and produce technical documentation and reports. Automation helps with change management, comparing document revisions and analysing test and measurement data. Creative problem-solving, plausibility checks and responsibility for technical safety remain with the experts. Using AI for routine work and as a sparring partner frees time for real engineering: developing concepts, evaluating variants and delivering robust solutions.

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?

  • Design and development engineers
  • Project engineers in plant and machine building
  • Test and validation engineers
  • Technical writers
  • Engineers in planning and engineering consultancies

Typical challenges we address

  • Evaluating extensive standards, guidelines and requirements efficiently
  • Producing technical documentation and reports alongside development work
  • Analysing and interpreting test and measurement data quickly
  • Managing changes traceably across many documents and stakeholders

What you will be able to do

  • You write structured prompts that reliably produce usable results for your day-to-day tasks.
  • You organise your work in Projects and revise longer documents efficiently with Canvas.
  • You analyse spreadsheets and files with data analysis and critically verify the results.
  • You build a custom GPT for a recurring task and share it with your team.
  • Use AI for requirements analysis, standards research and technical documentation.
  • Have AI create calculation and analysis scripts and validate them systematically.
  • Protect engineering know-how and observe product safety and liability requirements when using AI.

Course outline

Module 1 Foundations and effective prompting
  • How language models work: strengths, limitations and hallucinations
  • Building effective prompts with role, context, task and output format
  • Refining iteratively instead of starting over: follow-ups and correction loops
  • Choosing models and using web search and deep research appropriately
Module 2 Projects, Canvas and document work
  • Setting up Projects to group files, instructions and chats by topic
  • Summarising, comparing and interrogating long documents
  • Using Canvas to revise text and drafts section by section
  • Templates for emails, reports, minutes and presentation outlines
Module 3 Data analysis with spreadsheets and files
  • Uploading, cleaning and analysing Excel and CSV files
  • Calculating KPIs, replicating pivot logic and explaining variances
  • Generating charts and preparing them for presentations
  • Verifying results: checking calculation steps and sanity checks
Module 4 Custom GPTs for recurring tasks
  • Identifying and scoping good use cases for GPTs
  • Configuring instructions, knowledge files and conversation starters
  • Testing, improving and sharing a GPT within the workspace
  • Maintenance, change tracking for instructions and ownership in the team
Module 5 Privacy, workspaces and governance
  • Personal accounts versus Team and Enterprise workspaces
  • Settings for model training, memory, temporary chats and sharing
  • Handling personal and confidential data in line with GDPR
  • Internal usage policy and quality assurance for AI output
Module 6 Practice lab: Engineering
  • Structuring requirements
  • Standards and literature review
  • Calculation scripts
  • Test data analysis
  • Technical documentation
  • Change management support
Module 7 Data protection, quality & transfer to daily work
  • Design data, drawings and development results are trade secrets and may be subject to export control; they belong only in approved AI environments. Product safety and liability law apply unchanged to safety-related designs, so AI output must be verified and documented traceably.
  • 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

Structuring requirements

An AI assistant analyses customer requirements and specifications, spots contradictions, gaps and vague wording and turns them into a structured, verifiable requirements list for the design specification.

Standards and literature review

AI summarises relevant sections of standards, guidelines and technical papers, creates comparison tables and checklists and references the passages, which engineers check in the original.

Calculation scripts

Instead of coding formulas by hand, describe the calculation and receive a commented Python script or spreadsheet, for example for sizing, tolerance stack-ups or unit conversions, ready for testing and validation.

Test data analysis

Test bench data is cleaned, statistically analysed and visualised using AI-generated analysis code, anomalies are flagged and a draft test report is created.

Technical documentation

Design data, notes and previous documents are turned into drafts of operating manuals, maintenance handbooks, specifications and project reports with consistent terminology.

Change management support

AI compares document revisions, summarises technical changes clearly, identifies affected documents and interfaces and prepares change requests for the change board.

Prerequisites

Confident use of office applications and a web browser; no prior AI knowledge is required. Feel free to bring your own non-confidential sample tasks from your daily work. You have an engineering or technical background and work in development, design, planning or testing.

Technical requirements

A laptop with an up-to-date browser and a ChatGPT account; a paid plan or access to your organisation's Team or Enterprise workspace is recommended for custom GPTs, Projects and data analysis.

Data protection & compliance

Design data, drawings and development results are trade secrets and may be subject to export control; they belong only in approved AI environments. Product safety and liability law apply unchanged to safety-related designs, so AI output must be verified and documented traceably.

Suggested dates

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

  • Mon, 23 November 2026 Online Request
  • Tue, 1 December 2026 Kaiserslautern Request
  • Fri, 11 December 2026 Online Request
  • Thu, 17 December 2026 Bremen Request
  • Thu, 7 January 2027 Augsburg Request
  • Mon, 11 January 2027 Online Request

Frequently asked questions

Do I need a paid ChatGPT licence for the course?

You can follow the fundamentals with a free account. However, features such as custom GPTs, Projects with larger files and advanced data analysis are tied to paid plans or come with far more generous limits there. Ideally you join with access to your organisation's Team or Enterprise workspace, so you practise under real conditions.

May I enter confidential company data into ChatGPT?

That depends on your plan, your contractual terms and your internal policy. In Team and Enterprise workspaces, content is not used for training by default, but GDPR and confidentiality obligations still apply. In the course you learn how to classify and anonymise data and how to choose the right settings.

How is a custom GPT different from a saved prompt?

A GPT bundles persistent instructions, knowledge files and optional tools such as web search or data analysis into a dedicated assistant. Colleagues can use it without any prompting skills and get consistent results. A saved prompt, by contrast, has to be copied and supplemented with context every time, which is far more error-prone.

Can I use AI calculations for safety-related designs?

Only as support with full verification. Language models can get formulas, units or standard values wrong. Have calculation steps output as traceable code or formulas, check them against standards, hand calculations or validated software and document the check. Responsibility remains with you.

How do we protect design data?

Use only approved enterprise environments where inputs are not used for training. Also observe export control rules, for example for dual-use technologies, and agree with IT and export control which information may go into which tools. We discuss practical rules in the course.

How much does “ChatGPT for Engineers” 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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