Production

LLM Interaction Learning Games

Interactive games teaching enterprise teams how to work effectively with AI - from prompt engineering to understanding LLM capabilities

Azure AIGPT-4JavaScriptEnterprise TrainingLLM

LLM Interaction Learning Games

Built two interactive games to help enterprise teams develop practical AI skills—moving beyond "chat with a bot" to understanding how LLMs actually work in production systems.

The Problem

Everyone talks about AI transformation, but most employees have no mental model for how to actually use LLMs effectively. They either:

  1. Underuse: Treat AI as a novelty, never integrating it into real workflows
  2. Overuse: Expect AI to magically solve problems without proper setup
  3. Misuse: Apply AI to tasks where traditional approaches work better

We needed hands-on training that builds intuition, not just awareness.

Game 1: Prompt Engineering Challenge

Concept: Parse complex, messy data using only prompts. No code allowed.

Players receive real business documents—purchase orders, invoices, contracts—and must extract structured data using only natural language prompts to an LLM.

Mechanics

  • Levels: Progress from simple extraction to complex reasoning tasks
  • Scoring: Speed + accuracy + prompt efficiency (fewer tokens = more points)
  • Leaderboard: Competitive element drives engagement and knowledge sharing

What It Teaches

  • Prompt structure and specificity matter enormously
  • Chain-of-thought prompting for complex reasoning
  • When to use few-shot examples vs. detailed instructions
  • Cost awareness (token usage = real money at scale)

Game 2: Human vs. OCR Race

Concept: Compete against AI on data entry tasks.

Players race against GPT-4 Vision to extract data from scanned documents. Sometimes humans win. Sometimes AI wins. That's the point.

Mechanics

  • Side-by-side interface: human input vs. AI output
  • Real scanned documents with varying quality
  • Accuracy scoring with penalty for errors
  • Time pressure creates authentic workflow experience

What It Teaches

  • AI has specific strengths (consistency, speed on clear docs)
  • Humans have specific strengths (reasoning about ambiguous cases)
  • Quality of source material dramatically affects AI performance
  • Human-in-the-loop validation isn't overhead—it's essential

Technical Implementation

  • Frontend: JavaScript with real-time scoring
  • Backend: Azure AI Foundry for GPT-4 and GPT-4 Vision APIs
  • Documents: Real (anonymized) business documents from production systems
  • Deployment: Internal webapp accessible to all employees

Business Impact

  • Engagement: 85% completion rate across 200+ participants
  • Knowledge Retention: Post-training assessments showed 3x improvement in prompt quality
  • Cultural Shift: Teams started identifying AI opportunities in their own workflows
  • Cost Awareness: Reduced unnecessary API calls by teaching token economics

Design Philosophy

Games work because they make abstract concepts concrete. Instead of telling people "prompts matter," we let them discover it through failure and iteration.

The competitive element isn't about winners and losers—it's about creating shared vocabulary and experiences that teams reference in real work conversations.

Learning by playing. Understanding by doing.