Training course · Prompt Engineering · Founders & Startups

Prompt Engineering for Founders and Startups

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 Founders and Startups” is a hands-on course for roles such as Early-stage founders, Founding teams in incubators and accelerators and Early employees at startups. Rather than learning Prompt Engineering in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Founders & Startups.

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.

Founders have to juggle many roles with a small team and limited budget: product, sales, marketing, finance, hiring and investor relations. Generative AI acts like an extra team member. It helps with market research and competitor analysis, drafts pitch decks and investor updates, writes landing pages and sales emails, structures customer interviews and supports the financial model. With vibe coding, prototypes, clickable mock-ups and internal tools come together in hours rather than weeks, so hypotheses can be tested in the market much sooner. AI agents automate lead research, onboarding emails or support replies. The key is to combine speed with critical review and to get the legal basics right from day one.

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?

  • Early-stage founders
  • Founding teams in incubators and accelerators
  • Early employees at startups
  • Corporate venturing and intrapreneurship teams
  • Solo entrepreneurs with product ideas

Typical challenges we address

  • Little time and few resources for many parallel tasks
  • Hypotheses need to be validated quickly and cheaply
  • Heavy effort for pitching, investor relations and grant applications
  • No in-house experts for legal, finance and development in the early stage

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.
  • Validate business ideas faster with AI through research, interviews and prototypes.
  • Produce pitch decks, investor updates and sales materials efficiently with AI.
  • Build simple prototypes and internal tools through vibe coding and understand their limits.

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: Founders & Startups
  • Market and competitor analysis
  • Analysing customer interviews
  • Pitch decks and investor updates
  • Prototypes via vibe coding
  • Sales and marketing
  • Grants and business plans
Module 7 Data protection, quality & transfer to daily work
  • Startups must comply with the GDPR, imprint requirements and unfair competition law from the outset, for example for waitlists, newsletters and outreach. For AI-based products, the EU AI Act, copyright in generated content, code licences and the confidentiality of investor and customer data must be considered.
  • 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

Market and competitor analysis

AI structures market segments, target groups and competitors, derives positioning options and helps identify research sources – figures and facts are then backed up with reliable references.

Analysing customer interviews

Guides for problem interviews are created, notes are analysed for pain points, willingness to pay and quotes, and insights are condensed for the next iteration.

Pitch decks and investor updates

Storyline, slide copy and answers to typical investor questions are developed; regular updates for investors and advisers are generated from KPIs and milestones. Investors stay informed with little effort.

Prototypes via vibe coding

Landing pages, clickable prototypes and simple MVPs are built with AI coding tools to test demand and user behaviour before investing in professional development. Scaling up follows once demand is confirmed.

Sales and marketing

Ideal customer profiles, outreach sequences, website copy and social posts are written for the target audience and prepared in variants for A/B testing. Test results feed straight into the next iteration.

Grants and business plans

AI helps make sense of funding programmes, structure applications and business plans and explain financial assumptions clearly; the figures themselves are checked by an expert.

Prerequisites

Some initial experience with at least one AI assistant such as ChatGPT, Claude, Copilot or Gemini is helpful; no technical background is required. A startup idea or a role in a young company; 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

Startups must comply with the GDPR, imprint requirements and unfair competition law from the outset, for example for waitlists, newsletters and outreach. For AI-based products, the EU AI Act, copyright in generated content, code licences and the confidentiality of investor and customer data must be considered.

Suggested dates

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

  • Mon, 23 November 2026 Online Request
  • Fri, 4 December 2026 Munich Request
  • Mon, 7 December 2026 Online Request
  • Wed, 16 December 2026 Stuttgart Request
  • Fri, 22 January 2027 Online Request
  • Fri, 29 January 2027 Berlin 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 I build an MVP with AI without knowing how to code?

For landing pages, clickable prototypes and simple web apps, yes – often enough to test demand. For a scalable, secure product handling user data and payments, however, you need development expertise. The course shows where that line lies and how to hand prototypes over cleanly to developers.

Can investors tell a pitch deck was written by AI?

Generic copy stands out. What matters is that the storyline, numbers and insights come from your own work. AI helps you structure, sharpen and prepare for tough questions. In the course we practise making your story more compelling with AI without sounding interchangeable.

How much does “Prompt Engineering for Founders and Startups” 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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