Learning Path · Module 13 · AI Creator
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.
Who is this training for?
Interested parties with prior knowledge of Prompt Engineering, Low-Code and GenAI.
Bring this format straight into your team - tailored to your tools, your roles and your context for human-centered AI.
The training presents an approach to describing requirements and background knowledge that is understood and picked up in the language of software engineers and AI coding agents. Instead of describing concepts "intuitively," it demonstrates how to define specifications and documentation using established methods and terms, so that they can be more effectively incorporated into the final product.
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.
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.
So let's explore the possibilities for your digital product development together. We look forward to your inquiry.
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