Sorry, Wrong Door
Early LLM experiment with adaptive difficulty
An early LLM-powered game using a lying narrator and an adaptive difficulty engine that adjusts in real-time
The Challenge
Build one of the first LLM-powered games—with adaptive difficulty and a lying narrator—when almost no one knew it was possible.
The Solution
A deceptively simple door-choice game where an AI narrator provides hints about the correct path while lying 25% of the time, with a difficulty system that automatically adjusts “obviousness levels” based on real-time player success rates.
Adaptive Intelligence: AI That Learns from Player Behavior
Core Mechanics
- Binary choice system: Players choose between two doors to progress
- Controlled deception: AI lies 25% of the time, players know this creates trust/doubt tension
- Hint generation: LLM creates room descriptions containing clues about the correct door
- Real-time difficulty adjustment: System tracks success rates and modifies “obviousness level” to maintain target success percentages
Innovation in Early LLM Era
- Pioneering application: Built when first-generation LLMs were brand new, before most practitioners understood prompt engineering for games
- Dynamic prompt modification: AI receives different instructions based on room performance data
- Behavioral feedback loops: Player success/failure directly influences AI generation parameters
Game Design Psychology: Trust and Deception
Player Experience Innovation
- Known unreliable narrator: Players aware of 25% lie rate creates engaging psychological tension
- Adaptive challenge: Difficulty automatically adjusts to player skill, maintaining engagement without frustration
- Meta-gaming elements: Players must judge AI believability and hint obviousness
What Actually Happened
- Players developed strategies for detecting AI lies through linguistic patterns
- Difficulty system successfully maintained 65-75% success rates across different player skill levels
- Emergent trust dynamics as players learned to calibrate their confidence in AI hints
- Foundational insights into prompt engineering for consistent game mechanics
Why This Matters for Marketing Technology
Foundational Applications
- Adaptive content difficulty: AI that adjusts messaging complexity based on audience engagement
- Controlled narrative tension: Systems that balance truth and intrigue in brand storytelling
- Real-time personalization: Content that adapts based on user behavior patterns
- Trust calibration: Understanding how users develop confidence in AI-generated content
Key Insights
Key Insights
- Early LLM experiments revealed fundamental principles still used today
- Adaptive difficulty systems can maintain engagement across skill levels
- Controlled deception creates engaging psychological dynamics
- Real-time feedback loops enable AI systems that improve during use
The result: A foundational experiment in adaptive AI game design that demonstrated core principles of dynamic difficulty adjustment and controlled AI deception before these became standard practices.