Learning Path · Step 14 · UX 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 knowledge of prompt engineering is required.
Bring this format straight into your team - tailored to your tools, your roles and your context.
In this training, participants will build upon their beginner knowledge to learn new coding dialects and their advantages and disadvantages, in order to reach their goals faster together with AI. For example, it will be shown how problems that an LLM cannot solve can be addressed in other ways by generating the corresponding code.
In this training, participants will learn how to use any UX documents and texts (e.g., interview transcripts) as data sources thanks to Retrieval Augmented Generation (RAG) to answer natural language user 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 in Context Engineering (Knowledge Graphs and meta-prompting) will also be examined.
In a dialogue, 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 skillset, 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.
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