XYLOX Academy
XYLOX Academy
AI competence that actually works in the company.
The XYLOX Academy teaches practical AI competence for companies – structured ways of working, secure processes, prompting, research, data and legal topics as well as the sustainable integration of AI into everyday work.
About the Academy
What is the XYLOX Academy?
The XYLOX Academy teaches practice-oriented AI competence for companies. The focus is not only on using individual AI tools, but on structured ways of working, secure processes, prompting, research, data and legal topics as well as the sustainable integration of AI into everyday work.
The Academy model
A clear learning journey
The training is structured in stages – from understanding the basics to secure application in everyday work.
Fundamentals
Understanding what AI is, how language models work and where their limits lie.
- AI fundamentals & evolution
- Transformer & attention principle
- Recognising & checking hallucinations
Learning goal
Participants understand how modern AI systems work and their limits.
Application
Building solid practice in the AI model ecosystem and structured prompting.
- AI ecosystem & application fields
- Selecting language models purposefully
- Mastering the prompt blueprint
Learning goal
Participants use AI tools in a structured and targeted way.
Practice & security
Integration into real work processes with secure approvals and data protection.
- AI workflows & automation
- Data flow, AVV & server location
- Approval processes (human-in-the-loop)
Learning goal
Participants integrate AI safely and responsibly into everyday work.
Modules
Module overview
Eight modules – from AI fundamentals to data flow and compliance.
AI fundamentals & evolution
From theoretical beginnings to today's breakthrough: what AI is and how it developed.
- •Classify AI terms confidently
- •Follow the evolution of AI
- •Derive realistic expectations of AI
How language models work
Tokenisation, embeddings and the attention mechanism – the technical foundation of modern language models, explained clearly.
- •Understand tokenisation & embedding
- •Explain the attention principle
- •Follow context processing
Hallucinations & fact-checking
Why language models produce plausible-sounding but wrong answers – and how participants recognise and check them.
- •Recognise hallucinations
- •Apply fact-check duty
- •Check numbers, quotes & sources
The AI ecosystem
Six central application fields around the language model: text & LLMs, image/video/audio, AI agents, automation, APIs and custom assistants.
- •Overview application fields
- •Distinguish use cases
- •Assign suitable tools
Language models compared
Leading models (e.g. GPT, Claude, Gemini) in a neutral comparison – strengths and suitable use fields.
- •Distinguish model strengths
- •Choose the right model for the use case
- •Classify current developments
Prompt engineering
The prompt blueprint: role, context, task, format and example – five components for effective prompts.
- •Create structured prompts
- •Use components purposefully
- •Increase result quality reproducibly
AI workflows & automation
From trigger to verified result: how AI is integrated into structured work processes – with a checkpoint.
- •Build workflow steps
- •Structure AI processing
- •Integrate human review
Data flow, privacy & compliance
Where inputs go: red list, server location, AVV and human-in-the-loop as control points for safe AI use.
- •Apply control points
- •Maintain data protection
- •Establish secure approval processes
Competencies
What participants learn
Eight competency areas that truly carry into everyday work.
AI fundamentals
Understanding how AI systems work and their limits.
Prompt engineering
Structured, reproducible prompts following the prompt blueprint.
Advanced AI use
Targeted use of different models and application fields.
Research & data
AI-supported research with fact-based checking.
Communication & correspondence
Using AI for texts, drafts and editorial tasks.
Automation & routines
Turning recurring tasks into structured workflows.
Data & legal security
Data protection, AVV and secure data flows in the company.
Secure approval processes
Human review before sending and publishing.
Practice over theory
What is learned gets applied
The Academy is designed for practical application – every stage ends in real implementation, not just theory.
Learn
Structured learning content by curriculum and module handbook.
Apply
Practical tasks and workbooks for each module.
Practise
Cheat sheets and a prompt library for everyday work.
Check
Learning checks and comprehension review of the content.
Integrate
Sustainable integration of AI into real work processes.
For whom
Who is the Academy for?
The Academy is for everyone who wants to use AI responsibly in their work environment.
Employees
Practical AI competence for everyday work.
Managers
Sound understanding for well-founded AI decisions.
Companies
Standardised, uniform AI competence across the team.
Trainers & multipliers
Internal trainers who carry AI competence forward.
For companies
AI competence as a company standard
The Academy builds structured, secure AI competence in the company.
Academy for companiesStandardised AI competence
A uniform level of knowledge across teams.
Uniform ways of working
Shared methods and prompting standards.
Secure AI use
Data protection, data flow and approval processes reliably regulated.
Better productivity
Less manual work, more value-adding tasks.
Build internal AI competence
Knowledge that stays in the company.
Develop trainers & multipliers
In-house trainers who carry the Academy internally.
Academy system
A complete training system
Behind the Academy is a structured curriculum with accompanying materials.
Curriculum
Structured training build-up in stages and chapters.
Module handbook
Overview of all modules with learning goals.
Workbooks
Accompanying material for each module.
Cheat sheets
Compact quick guides for everyday work.
Prompt library
Tested prompts for typical use cases.
Trainer handbook
Foundation for internal multipliers.
FAQ