Local AI Training for Developer Teams

For whom:

This Module is designed for companies that want to understand the methodology behind an AI potential analysis—knowledge that your team can apply on its own in the future.

Content:

• How do you generally identify AI use cases in business models, processes, and data?
• Evaluation criteria for use cases: benefits, feasibility, data protection/compliance, cost-effectiveness
• Overview of common technologies and architectural approaches
• Typical pitfalls in AI implementation and how to avoid them

Outcomes:

• An understanding of the methodology for systematically evaluating AI potential
• A practical framework that your team can independently apply to future use cases.
  
  (Note: This module provides a general overview of the methodology. For a customized, documented analysis of your company, we offer our AI workshop.)

For whom:

This module is designed for companies that want to understand how large language models work and which deployment options (from the cloud to on-premises) are best suited to their use case.

Content:

• Technical fundamentals of LLMs: embeddings, attention, context window, system prompts, reasoning models
• Pretraining and fine-tuning
• Overview of deployment options: cloud, on-premises, private cloud, hybrid
• On-premises LLMs with Ollama as a possible option: model selection and use cases
• Integration into existing systems: APIs, access concepts, user roles; security and data protection requirements depending on the infrastructure

Outcomes:

• A solid understanding of how LLMs work
• An assessment of which model and deployment options—including on-premises AI—are best suited to your use case
• Concrete next steps for your technical implementation

For whom:

This module is designed for companies that want to effectively link their own corporate data with large language models (LLMs) to achieve more precise and context-aware results.

Content:

• How RAG works: embeddings, vector databases
• The interplay between prompts and retrieval
• Applications for internal company data—examples and use cases
• Prompt engineering: fundamentals, advanced techniques, collaborative solutions

Outcomes:

• Understanding of the RAG architecture and how it works
• Concrete ideas for using RAG with your own data