Escape the Hype: Teaching LLM Concepts Through an Interactive AI Factory Game

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