1. basics of OpenAI & models
- Overview of the OpenAI ecosystem.
- Licenses and billing modalities (also explain API).
- Concepts and functionality of the models (GPT-5, GPT-5 Thinking, GPT-4o, alternatives).
- Select models for different purposes.
2. tools & application scenarios
- Chat, Deep Research, Internet Search, Canvas, Study & Learn.
- Prompt and context engineering.
- Decision support: which tool to use when?
3. output formats & automation
- PowerPoint, Excel, PDF, Markdown, HTML, roadmaps, tables.
- Data analysis: import, cleansing, statistics, visualization.
4. advanced functions & strategies
- Custom GPTs: creation & customization, areas of application.
- Projects: structured work, knowledge building, content productions.
- Agent mode: autonomous work on complex tasks.
- Voice Assistant: voice-based interaction, own use cases.
- Assistant teams: define roles, combine results, iteration.
5. optimal use & strategies
- Settings: Profiles, memory, data protection, output formats.
- Context provision: upload documents, define roles & goals.
- Create long formats (e.g. books, reports, training material).
- Combine queries, link tools (Deep Research + Canvas + DALL-E).
6 OpenAI Playground
- Overview and possible applications.
- Compare models, set parameters.
- Prompt optimization, testing of ideas, simulation of custom GPTs.
- Best practices: when Playground instead of ChatGPT-UI?
7. strategies & examples from practice
- Step-by-step procedure: Define target → Select tool → Prompt strategy → Validation.
- User stories: marketing campaign, market analysis, training material, book project.
8. best practices & future
- Success factors (prompts, tool selection, working methods).
- AI Governance & Ethics.
- Outlook for future developments.