Fast, general-purpose classifiers like Jev can be used as temporary solutions for specific tasks, then replaced with cheaper, faster custom classifiers trained on the input-output data the Jev instance generated. This creates a practical path for engineering teams without ML expertise to eventually build optimized task-specific models.

• System One models solve the immediate problem of deploying classifiers without ML expertise or large datasets — you just prompt them like an LLM
• Once a System One model proves a feature works, you can collect its decisions as training data to build a specialized classifier that's faster and cheaper to run
• This pattern lets teams validate whether a feature is worth building before investing in custom ML development, reducing risk and upfront cost

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.