Training course · Google Gemini · Engineering

Google Gemini for Engineers

Make productive use of Google Gemini and Gemini in Workspace: Docs, Sheets, Gmail, Meet, custom Gems and NotebookLM for your sources.

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

“Google Gemini 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 Google Gemini in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Engineering.

For organisations running on Google Workspace, Gemini is the natural AI assistant: it is available as a standalone app and is also built directly into Gmail, Docs, Sheets, Slides, Drive and Meet. In this practical day you learn to combine both access points deliberately. You summarise email threads and draft replies, create document drafts based on existing files in Drive, structure and analyse spreadsheets in Sheets and have Meet take meeting notes automatically – always with a focus on your own day-to-day workflows.

You also build your own Gems: specialised assistants with fixed instructions and knowledge files that standardise recurring tasks across the team. With Deep Research you produce well-founded research reports, and you use NotebookLM as a source-grounded workspace that answers only from your own documents and cites the relevant passages. A dedicated module covers data protection and administration in Workspace environments. You leave with tested prompts, a working Gem and a clear picture of which Google tool suits which task.

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 use Gemini in Gmail, Docs, Sheets, Slides and Meet for your recurring tasks.
  • You write prompts that bring existing Drive files and emails into the context.
  • You build a custom Gem and share it with your team.
  • You work source-grounded in NotebookLM and obtain referenced answers from your own documents.
  • 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 Gemini fundamentals and effective prompts
  • The Gemini app and Gemini in Workspace: access points and use cases
  • Prompts with task, context, format and references to Drive files
  • Web search, Deep Research and checking sources
  • Limitations, hallucinations and critical evaluation of output
Module 2 Gmail, Docs and Slides
  • Summarising email threads and drafting context-aware replies
  • Drafting, rewriting and tailoring documents in Docs
  • Pulling in and combining content from Drive files
  • Creating presentation content and images in Slides
Module 3 Sheets and Meet
  • Structuring tables, generating formulas and cleaning data
  • Analyses, summaries and charts in Sheets
  • Letting Meet take meeting notes automatically
  • Deriving action items and decisions from meeting notes
Module 4 Gems and NotebookLM
  • Creating custom Gems with instructions and knowledge files
  • Testing, improving and sharing Gems with the team
  • NotebookLM as a source-grounded workspace for your own documents
  • Generating summaries, study aids and audio overviews from sources
Module 5 Data protection, administration and rollout
  • Privacy commitments for Workspace customers versus personal accounts
  • Drive sharing settings and how they affect Gemini results
  • Handling personal and confidential data in line with GDPR
  • Usage policies and quality assurance within the team
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 everyday use of Google Workspace (Gmail, Drive, Docs, Sheets); no prior AI knowledge is required. 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 Google Workspace account with Gemini features enabled; alternatively a Google account for the Gemini app and NotebookLM.

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.

  • Thu, 26 November 2026 Online Request
  • Mon, 7 December 2026 Bremen Request
  • Fri, 8 January 2027 Augsburg Request
  • Thu, 21 January 2027 Online Request
  • Thu, 4 February 2027 Salzburg Request
  • Tue, 16 February 2027 Online Request

Frequently asked questions

What is the difference between the Gemini app and Gemini in Workspace?

The Gemini app is a standalone chat assistant for research, writing and analysis. Gemini in Workspace works directly inside Gmail, Docs, Sheets, Slides and Meet and can draw on your emails and Drive files. In practice they complement each other: the app for open questions, the integrated version for work on specific documents.

Why would I use NotebookLM in addition to Gemini?

NotebookLM restricts its answers to the sources you upload – policies, studies or project files, for example – and points to the relevant passages. This reduces hallucinations and works well for getting up to speed on a topic, building internal knowledge collections or preparing training material. Gemini is broader in scope and can also draw on the web.

Is my Workspace data used to train Gemini?

For Google Workspace customers, Google commits that content from Gmail, Drive and other services is not used to train models outside your domain without permission. Different rules apply to personal accounts. Either way, GDPR and your internal policies remain decisive – in the course we review the relevant settings together.

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 “Google Gemini 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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