Training course · Generative AI · Founders & Startups

Generative AI for Founders and Startups

Vendor-neutral, hands-on day: how language models work, where they go wrong and how to roll out AI successfully in your team.

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

“Generative AI for Founders and Startups” is designed for roles such as Early-stage founders, Founding teams in incubators and accelerators and Early employees at startups and shows step by step how to use Generative AI productively, safely and transparently in Founders & Startups – from the fundamentals to advanced workflows.

Generative AI has reached most organisations, yet teams often lack a shared understanding of what language models can actually do and where their limits lie. In this vendor-neutral course you will learn how large language models generate text, why they hallucinate and how careful prompting, context and source checking lead to reliable results. Rather than relying on any provider's marketing claims, you will work with transparent criteria that help you compare tools and judge their fit for your own tasks.

The second focus is everyday implementation. You will identify concrete use cases in your area of work, weigh benefit, risk and effort, and draft a realistic roll-out plan for your team. This covers ground rules for handling confidential data, a practical prompt library and ways to build acceptance and make results measurable. Throughout the day you work with examples from your own field, so you leave with an actionable roadmap rather than a set of abstract ideas.

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 can explain clearly how language models generate text and why hallucinations occur.
  • You can write prompts that consistently deliver useful, verifiable results.
  • You can compare AI tools against transparent criteria covering data protection, features and cost.
  • You can identify and prioritise concrete use cases for your area of work.
  • 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 large language models work
  • Tokens, context windows and probabilities: what happens when a model generates text
  • Training knowledge versus web search versus your own documents (RAG)
  • Text, images, audio and code: what multimodal models can do today
  • Realistically assessing typical strengths and weaknesses in office work
Module 2 Spotting hallucinations and getting reliable output
  • Why models produce plausible-sounding errors and when the risk increases
  • Prompting techniques: role, context, examples, output format and step-by-step instructions
  • Requesting sources, cross-checking facts and introducing a four-eyes principle
  • Scoping tasks so that AI assists rather than decides
Module 3 Choosing tools without vendor bias
  • Overview of chat assistants, AI built into office suites and specialist tools
  • Criteria: data processing, hosting location, admin features, integrations and pricing model
  • Enterprise licences, API access and locally hosted open-source models compared
  • Building a simple scoring matrix for your own tool selection
Module 4 Identifying use cases in your field
  • Systematically capturing recurring writing, research and analysis tasks
  • Rating benefit, risk and implementation effort for each use case
  • Distinguishing quick wins from strategic initiatives
  • Creating a prioritised use-case backlog for your team
Module 5 Rolling out generative AI in your team
  • Usage rules: which data may go into which tool and who signs off results
  • Building and maintaining a shared prompt library and templates
  • AI champions, training formats and peer exchanges to embed new habits
  • Measuring success: documenting time saved, quality and user satisfaction
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

No technical background is required. It helps to bring along typical tasks from your day-to-day work that you would like to support with AI. A startup idea or a role in a young company; no programming skills required.

Technical requirements

You will need a laptop with an up-to-date browser and internet access. A free account with a common AI assistant is sufficient; company licences are welcome.

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.

  • Wed, 25 November 2026 Online Request
  • Thu, 10 December 2026 Bremen Request
  • Thu, 14 January 2027 Salzburg Request
  • Thu, 28 January 2027 Online Request
  • Fri, 12 February 2027 Kaiserslautern Request
  • Mon, 22 February 2027 Online Request

Frequently asked questions

Is the course tied to a specific AI tool?

No. The training is deliberately vendor-neutral. You work with widely used chat assistants and learn principles that apply to all major language models. That way you can transfer your skills to whichever tool your organisation has approved and evaluate new offerings yourself using clear criteria instead of relying on marketing statements.

How is confidential company data handled during the course?

For the exercises you use anonymised or fictitious examples from your area of work. At the same time you learn which categories of data may be entered into which tools, what to look for regarding data processing agreements and use of inputs for training, and how to set out these rules clearly for your team.

Will we leave with something concrete to implement?

Yes. You leave with a prioritised use-case backlog, a scoring matrix for tool selection, tested prompt templates and a draft set of team usage rules. These materials are designed so you can take them straight into internal discussions with management, IT and your data protection officer.

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 “Generative AI 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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