Learning Path · Module 14 · AI Creator
This training deepens prompt engineering practices. It teaches how to use JSON schemas to force specific output structures to be reproducible and machine-readable. Additionally, it demonstrates how to formulate metaprompts and prompts that check themselves for quality.
Who is this training for?
Interested parties who wish to acquire further competencies in this topic. Prior basic knowledge of prompt engineering is required.
Bring this format straight into your team - tailored to your tools, your roles and your context for human-centered AI.
In this training, participants will build upon their beginner knowledge to learn new coding approaches (Workflow Automation vs. Vibe Coding) and their advantages and disadvantages, in order to reach their goals faster together with AI. For example, it will be shown how certain processes can be automated with n8n using Small Language Models, targeted Vibe Coding, or even entirely without AI.
In this training, participants will learn how to use any documents and texts (e.g., interview transcripts or manuals) as data sources thanks to Retrieval Augmented Generation (RAG) to answer natural language questions. Additionally, participants will learn how to structure the AI pipeline and source documents to ensure the RAG approach delivers the most accurate results possible. The limitations of RAG and suitable alternatives such as knowledge graphs and LLM wikis will also be examined with their respective advantages and disadvantages.
In a dialogue with humans, an AI can reach its full potential by understanding what users want and responding in an easy-to-understand manner. However, "out-of-the-box" AIs are trained to follow any path a user might take. Creating a guided communication path, or one that scales according to the user's abilities and prior experiences, is not easy. This training reveals how to design conversations with AI so that they are guided toward a specific user goal.
So let's explore the possibilities for your digital product development together. We look forward to your inquiry.
Thank you! Your submission has been received!
