The author demonstrates two practical techniques for programming with System One models (fast classifiers that pick from multiple choices): tiered goal-setting for sequential decision-making, and tournament sampling for choosing among many options. He shows these approaches working in Doom gameplay and Wikipedia navigation tasks.

• Tiered goals work better than single-pass decisions: periodic prompts asking the model to choose short-term goals (e.g., "kill enemies" vs "collect armor") make it perform more intelligently than just reacting to immediate game state every 200ms.
• Tournament sampling beats confidence scoring when picking from many options: feeding 100 links at a time, then narrowing down, found the optimal path in seconds, while trying to score all 1,000+ links failed badly.
• System One models offer a practical alternative to tool calls for real-time systems where you need predictable latency and don't need full language generation flexibility.

System One models are stripped-down LLMs that only output multiple-choice answers, trading flexibility for speed and predictability. The author shows two practical techniques for building real-time systems with them: layered goal-setting for sequential decision-making and tournament sampling for choosing among many options.

• You can convert any LLM into a fast classifier by batching single-token outputs with structured prompts—no model retraining needed, just inference-level changes.
• Tiered goals (asking the model to pick short-term objectives before making immediate decisions) dramatically improves performance in tasks like game-playing by giving the model more compute to reason about strategy.
• Tournament sampling—splitting large choice sets across multiple rounds rather than trying to rank everything at once—works better than absolute scoring because LLMs judge relative quality more reliably than absolute confidence.