Corporate GPT: Tailored use of LLMs in companies
Integration of proprietary LLM, RAG systems, and AI agents
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Machine Learning Engineer
Basics
- Introduction to AI and Large Language Models (LLMs)
- Reasoning and agents
- Use cases, possibilities, and benefits of proprietary GPTs
- Creating a custom GPT
Design and creation of customized chatbots
- RAG (Retrieval-Augmented Generation) explained in detail
- Application scenarios in companies and use cases for users
- Examples and best practices of established in-house chatbots
- Prompting, RAG, and fine-tuning compared
- Further customization options for LLMs
- Vector embedding and vector databases
- Creating a RAG-powered chatbot
Tools, Workflows & Agents, MCP
- Workflows for more complex tasks
- What are agents and what technology lies behind them?
- How workflows and agents use tools
- MCP and A2A – uniform agent interfaces for tools
Design principles for AI products
- What makes a good AI product?
- Best practices for designing AI products
- AI as a rapidly changing tool
- Risk management in the context of agentic AI
- Identify bottlenecks with logging and metrics
- Assess model performance using benchmarks
AI sovereignty and closed vs. open source
- Closed source implementation via API
- Open source implementation via API or self-hosted
- Open source/open weights vs. closed source models
- decision factors
Competencies and implementation
- Agents and chatbots as part of the AI strategy
- Data protection and the EU AI Act in the context of LLMs and agents
- AI competency requirements in the context of LLMs and agents
- Team structures and roles
- Exercise for identifying use cases
- Important note on the pre-installation of software
Important note on the pre-installation of software
The training many practical exercises that allow you to try out the techniques described. You will perform these exercises on your own computer. We therefore recommend that you participate in training a laptop or PC; the use of a tablet is not recommended.
In your online learning environment, you will find useful information, downloads and extra services for this training course once you have registered.
You will acquire comprehensive knowledge about LLMs, agentic AI, and the use of AI in a business context.
You will learn how to create RAG-powered chatbots and connect LLMs to your specific company data.
You will learn how to integrate agents into automated workflows using tools and protocols.
You understand what is important when designing robust AI products and how to manage risks in the context of LLMs and agents.
You will learn about the strategic aspects that are important when using LLMs and agents in your company.
The content of this training supports the obligation to provide evidence of the promotion of AI competence within the meaning of Art. 4 EU AI Regulation.
This training conducted in a group of no more than 12 participants.
Individual support from the trainers guaranteed. The practical exercises are carried out together in the group.
If you have any questions, the trainers are trainers you—in the classroom or through individual support.
Once you have registered, you will find all the information, downloads and extra services for this training course in your online learning environment.
This training for specialists and managers who deal with the use of AI solutions in their companies, as well as for developers the field of artificial intelligence and machine learning. In this training , what you learn training put directly into practice (hands-on): you will create your own workflows and agents using no-code/low-code tools. Therefore, a basic affinity for technology and an interest in technical understanding are required.
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