Centigrade Academy
From first orientation to your own AI toolchain: our formats build on each other. Find your station - and grow from there.
Station 1
You want to understand what AI means for UX - and speak about it with confidence.
Participants will learn how UX and AI can complement each other positively by automating or assisting everyday UX tasks with AI. In addition, participants will learn corresponding prompts, and potentials will be highlighted as to where AI also opens up new, previously undiscovered possibilities (e.g., interviews with AI personas or generation of visual information architecture models without graphical tools).
From the correct derivation of requirements from given use cases in the form of scenarios, user needs, epics, and user stories, to the creation of an AI assistant in the MS Copilot environment. This includes, among other things, the creation of AI persona agents to verify use cases from a requirements and interface perspective.
The training offers a wide spectrum, from the basics of prompt engineering to insider tips and tricks for professional prompts in the UX field. Prompting frameworks such as C.R.I.S.P.E., A.P.E., or R.A.C.E. and their use in daily UX work, combined with their application in various services like ChatGPT or MS Copilot, will be covered. Participants will gain confidence in prompting and also learn how to create, reuse, and share prompt templates with colleagues.
Whether it is AI usage, vibe-coding, automation, or the specification of requirements: nowadays, one repeatedly reaches a point where a basic understanding of code is crucial to effectively achieve one's goals within a team. This training therefore introduces the basics of low-code and specifically addresses people who have had little to no programming knowledge so far.
2025 has already been proclaimed as the year of "AI agents" as the next evolutionary stage of GenAI. Agents possess a memory, can perform tasks autonomously, formulate plans, use tools, and collaborate with one another. This training demonstrates the opportunities and risks of using AI agents to increase efficiency and automate complex problem-solving, and prepares participants for the skillset required to successfully "keep AI agents in check."
Participants will learn how to make UX successes and failures measurable and visible for their stakeholders. An overview of common UX metrics and KPIs will be provided, and the advantages of usage data tracking and evaluation will be highlighted.
Station 2
You already use AI and want to integrate it systematically into your UX work.
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.
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.
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.
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.
Station 3
You build AI-powered products and processes - from the toolchain to the architecture.
Participants will learn how to derive user needs from usage scenarios and to verify them in the field successfully and convincingly according to the INVEST principle. Additionally, the training demonstrates how verified user needs are converted into robust user requirements in the form of agilely plannable user stories. It will also be shown how user stories can be split into self-explanatory and independently developable units, thereby reducing project risks and prioritization efforts.
Intensive course with content from the focus areas of UX, Scrum, and AI, including: Automatic generation of user stories and epics based on user research results; brainstorming and ideation with virtual user personas; use of Retrieval Augmented Generation (RAG) methods to increase understandability and provide automatic summaries of existing sprint and milestone planning, and prompt engineering methods for the quality assurance of existing user requirements.
Jeff Bezos once said: "Amazon must be an API-first company!" and implemented this vision more than successfully. But what is an API anyway, and how do you design or develop an API within your own corporate context? This training provides answers and is aimed at participants who want to make their solutions maximally usable for all colleagues, including software engineers and low-coders.
Even if a certain level of competence in using ChatGPT, Claude, Midjourney, or other GenAI tools seems to have developed by now: in the end, a single model never solves the underlying problem on its own. As always, it depends on the combination of the right tools at the right time; otherwise, only fragmented results are produced that do not contribute to a common goal. The training teaches how to link complex AI toolchains along the UX process using a low-code environment for various problem statements, enabling them to solve complex problems automatically, reproducibly, and in a way that is accessible to other colleagues or further toolchains.
Agents prefer to communicate via MCP servers. But what exactly is an MCP server and how does it work at its core? Why can they solve complex problems across application boundaries and how do you control their workflows and orchestration through clever handoffs and boundaries of responsibility? Answers to these questions will be provided in this deep-dive training.
Every department has hundreds of different use cases that can be partially automated or qualitatively improved. However, many of these use cases require advanced skills in prompt engineering and low-code automation. This training is therefore aimed at participants who have already developed these skills and now wish to apply them effectively within their own departmental context.
We also assemble the learning path as an in-house programme - tailored to your team's roles, tools and maturity. From a single workshop to a multi-month curriculum.
So let's explore the possibilities for your digital product development together. We look forward to your inquiry.
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