Training course · Prompt Engineering · Automotive

Prompt Engineering for the Automotive Industry

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 the Automotive Industry” is a hands-on course for roles such as Engineering and requirements management staff, Purchasing and supplier management at OEMs and suppliers and Quality management and complaint handling. Rather than learning Prompt Engineering in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Automotive.

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.

The automotive industry is under immense pressure to change: electrification, software-defined vehicles, new competitors and demanding regulation are transforming manufacturers, suppliers and dealerships alike. Generative AI supports almost every stage of the value chain – from requirements management in engineering, through supplier communication and complaint handling in purchasing and quality, to sales and aftersales at the dealership. AI assistants help unlock specifications, standards and test reports, structure 8D reports or answer customer enquiries. AI agents connect data from different systems. What matters is protecting engineering know-how, meeting quality and safety standards and having clear approval processes.

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?

  • Engineering and requirements management staff
  • Purchasing and supplier management at OEMs and suppliers
  • Quality management and complaint handling
  • Sales, service and aftersales at dealerships
  • Production and logistics planning

Typical challenges we address

  • Extensive specifications, standards and requirement documents
  • Heavy documentation workload in quality management and complaints
  • Complex supply chains requiring constant coordination
  • Strict protection of engineering data and intellectual property

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.
  • Analyse technical documents such as specifications and standards with AI in a structured way.
  • Accelerate quality, complaint and supplier processes with AI support.
  • Use AI while upholding confidentiality, quality standards and approval processes.

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: Automotive
  • Analysing specifications
  • Complaints and 8D reports
  • Supplier communication
  • Unlocking standards and processes
  • Sales and aftersales
  • Training and technical documentation
Module 7 Data protection, quality & transfer to daily work
  • Engineering data, design documents and customer specifications are subject to strict confidentiality agreements and industry information security requirements such as TISAX, and may only be processed in approved AI environments. Customer data at dealerships falls under the GDPR, and safety-relevant content always requires expert sign-off.
  • 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

Analysing specifications

AI extracts requirements from lengthy customer specifications, compares them with the supplier's technical response, flags contradictions and ambiguities and drafts clarification questions for customers or development partners.

Complaints and 8D reports

Complaint descriptions are structured, potential causes gathered using methods such as Ishikawa and 8D reports written in clear language – root cause analysis remains the expert team's job.

Supplier communication

Requests, formal notices, escalations and audit reports are written precisely and in several languages, supplier ratings are commented on and meeting preparation is produced. Agreements are recorded for follow-up.

Unlocking standards and processes

Internal process descriptions, work instructions and quality requirements are made searchable, so staff receive answers with a reference to the governing document. New staff find applicable rules much faster.

Sales and aftersales

At the dealership, AI drafts offers, vehicle descriptions, replies to customer enquiries and service reminders and helps service advisers explain repairs clearly. Customer requests are captured for follow-up in the CRM.

Training and technical documentation

Training materials for assembly, service or new vehicle technologies are created from technical documents, simplified for the audience and translated into other languages. Quiz questions per chapter are included.

Prerequisites

Some initial experience with at least one AI assistant such as ChatGPT, Claude, Copilot or Gemini is helpful; no technical background is required. Professional experience at a vehicle manufacturer, supplier or dealership; 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

Engineering data, design documents and customer specifications are subject to strict confidentiality agreements and industry information security requirements such as TISAX, and may only be processed in approved AI environments. Customer data at dealerships falls under the GDPR, and safety-relevant content always requires expert sign-off.

Suggested dates

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

  • Fri, 27 November 2026 Online Request
  • Thu, 10 December 2026 Berlin Request
  • Tue, 15 December 2026 Online Request
  • Fri, 8 January 2027 Stuttgart Request
  • Wed, 13 January 2027 Online Request
  • Wed, 27 January 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 specifications be uploaded to AI tools?

Only into environments approved by your information security team that meet your customers' requirements. Many OEMs regulate this contractually. The course shows how to work with approved enterprise solutions, clean documents beforehand and which content never belongs in external services.

Can AI carry out a root cause analysis?

AI helps gather potential causes systematically, structure methods such as 5 Whys or Ishikawa and write reports. The actual analysis with measurements, tests and process knowledge remains the expert team's task. AI speeds up the path there and improves documentation quality.

How much does “Prompt Engineering for the Automotive Industry” 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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