, Helium [3rd Floor]
Everyone talks about LLMs, RAG, and AI agents - but who truly understands them? Marketing promises magic while documentation assumes expertise. Recent research from Gartner reveals the consequences: only 8% of HR leaders believe their managers possess adequate AI competency, while companies that restructure work around AI achieve revenue goals twice as often as those who merely train employees. The problem isn't lack of information; it's the lack of genuine understanding through experience.
We took a different approach. Instead of slides or tutorials, we built "AI Factory" - a non-profit educational platform in the form of escape room game where players learn by doing. Craft prompts under budget pressure. Watch guardrails fail in real-time. Break their own RAG pipeline. Each mistake teaches more than any documentation ever could.
In this talk, we'll share what we discovered while building and testing this game with real users: why failure-driven learning outperforms tutorials, how game mechanics create memorable "aha moments," and the surprising concepts that clicked only through play.
The gap between AI adoption and AI understanding keeps growing. Teams copy-paste prompts without understanding why they work, vendor materials highlight capabilities over limitations, and the EU AI Act now requires organizations to ensure "a sufficient level of AI literacy among their staff." Traditional training — documentation, tutorials, talks — isn't closing this gap. What's missing is embodied learning: touching the parameters, breaking the system, feeling the consequences.
Our Approach
We built "AI Factory" — a Python-based educational game where players learn LLM concepts through hands-on challenges. Set in a magical potion factory, players master prompt engineering, guardrails, RAG pipelines, MCP tool orchestration, and multi-agent coordination.
What makes it different from typical AI tutorials:
- Real API calls, not simulations. Players interact with actual LLMs — when they misconfigure guardrails or adjust temperature, they see real consequences that transfer directly to production.
- Budget-driven decisions. Every API call costs in-game currency, forcing the same quality-cost-speed tradeoffs faced in real deployments.
- Progressive disclosure over information dumps. Each game stage reveals one missing piece. The full picture only clicks at the end — and that revelation is the reward.
- Immediate, specific, actionable feedback. Players see results the moment they submit — not just "incorrect," but a diagnostic breakdown of exactly what went wrong, clear enough to act on and retry.
What This Talk Covers
We share concrete design decisions and their outcomes — what worked, what didn't, and what surprised us:
- Narrative vs. jargon. How story-driven framing changed the way players understood complex concepts like RAG — without a single slide of theory.
- Constraints as a teaching tool. Why our first budget system backfired, and how a small redesign turned frustration into strategic thinking.
- When to simulate instead of build. Where we replaced real infrastructure with controlled illusions — and why the learning outcome didn't suffer.
- One game, many audiences. How players from different backgrounds found completely different entry points into the same levels.
- Scoring on top of non-deterministic AI. How we built a reliable evaluation engine for a system that never gives the same answer twice.
Who Should Attend this Talk
This talk is designed for multiple audiences:
- Educators and trainers looking for new approaches to teaching AI concepts
- Team leads responsible for upskilling teams on AI fundamentals — take away a tested approach, not just theory
- Anyone interested in gamification as an approach to technical education
I'm a Data Scientist based in Munich who believes AI should be understood, not feared. After earning my Master's at LMU Munich, I've spent the past five years turning complex ML challenges—from computer vision to agentic systems—into working solutions. But what really excites me is making AI click for others: whether through hands-on workshops or building interactive experiences that turn abstract concepts into "aha!" moments. When I'm not wrangling models, you'll find me exploring ways to gamify learning and bridge the gap between cutting-edge AI and everyday understanding
I am a data scientist at Steadforce, building LLM and agent workflows with Python from cloud to edge. My current focus is AI literacy: helping teams understand what LLMs, RAG, and agents actually do beyond the hype. I co-designed “AI Factory,” a game where players break and fix AI systems to build real intuition.
I'm a Data Scientist who enjoys turning complex systems into practical, intuitive solutions. After earning my PhD in Mathematics I’ve spent my career turning complex scientific ideas into practical computational tools. My work ranges from exploring the frontiers of GenAI to building semantic data layers, and I spend much of my time developing scientific software and digital twins for real‑world processes. I love creating tools that make sophisticated models understandable and usable, bridging the gap between deep technical detail and everyday application.