Learning Path · Step 13 · AI Creator
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.
Who is this training for?
Interested parties with prior knowledge of Prompt Engineering and GenAI.
Bring this format straight into your team - tailored to your tools, your roles and your context.
The training presents a more technical approach to UX that is understood and respected in the language of software engineers. Instead of describing concepts "intuitively," it demonstrates how to define them using established methods and terms from the world of software engineering.
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.
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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