AI Solution Architect

The AI ecosystem & strategic selection

• Content: Current LLM landscape (Gemini, GPT, Claude, Open Source). Selection criteria: Latency vs. quality vs. cost.
• Practical task: Creation of a decision matrix for a specific company scenario (When do I use a small local model, when a powerful cloud LLM?).

Architecture of language models & system prompts

• Content: Token management, Context Windows and the art of “Programmatic Prompting”. Building robust system instructions (guardrails) that do not “break out”.
• Practical task: Design a system prompt for a company assistant that strictly adheres to internal guidelines and blocks unwanted topics.

Local AI & data protection infrastructure

• Content: Deep dive into local solutions (Ollama, LM Studio). Hardware check: What does a company laptop/server need?
• Practical task: Installation and local test of a model; measurement of the response speed with different hardware settings.

Enterprise Integration & Copilots

• Content: Technical control of Microsoft 365 Copilot & Google Workspace. API connections (custom GPTs / actions).
• Practical task: Configuration of an “action” (interface) with which a chatbot can retrieve data live from an Excel cloud table.

IT security, ethics & rollout management

• Content: Security (prompt injection), EU AI Act (technical documentation obligations). Change management: How do I involve the IT department and users?
• Practical task: Creation of a technical compliance checklist for the rollout of a new AI tool in the company.

Knowledge management with RAG (Part 1: Data pipeline)

• Content: How does company data get into the AI’s brain? Chunking (cutting up documents) and embeddings (vectors). Basics of vector databases.
• Practice task: Preparing a document set: Strategic chunking (by paragraph vs. by page) and discuss the pros/cons for search quality.

Knowledge management with RAG (Part 2: Quality & Evaluation)

• Content: Improving retrieval quality. How do I technically prevent hallucinations? Evaluation: How can I recognize that my system is responding “well”?
• Practical task: Test run of a RAG pipeline: Comparing the AI answers with the original sources and identifying sources of error.

Agentic AI – from response to action

• Content: Introduction to autonomous agents. “Function Calling” explained simply: How AI learns to use tools (e.g. CRM query, SAP entry).
• Practical task: Definition of a simple “toolbox” for an AI (Which interfaces in the company are ready for AI automation?).

Low-code automation & n8n

• Content: Introduction to n8n as the “glue” between AI and business software. Workflow design: From email to AI analysis to the finished process.
• Practice task: Building an automated workflow that classifies a customer request and creates a personalized response draft.

• Certificate statutes
• Rules for Participation
• Module handbook