Scientific Definition
How much input the model can process at once.
Plain-English Definition
How much input the model can process at once.
Feynman Explanation
Bigger context, more capability, more cost.
Core Principle
How much input the model can process at once.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Bigger context, more capability, more cost.
Examples
- 1M token context windows in newer models.
- Architecture decisions for document-heavy work.
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Famous Experiments
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Design Principles
- Don't over-index on long context. Retrieval often beats stuffing.
Measurement Approaches
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Evidence
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The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Don't over-index on long context. Retrieval often beats stuffing.
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Interactive Mini Network
Click any neighbor to re-center the graph and follow the threads of connection.
Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
AI system that takes actions to achieve goals, often across tools.
Designing processes from scratch around AI capability.
The relationship between inputs and outputs changes.
Underlying data distribution changes over time.
Vector representation of content for similarity and search.
Evaluation suites that no longer reflect real-world conditions.
Systematic testing of model quality, safety, and capability.
Models learning from examples in the prompt.
How much delay the user experience tolerates.
Train a smaller model to imitate a larger one.
Performance degradation as real-world data shifts.
Multiple specialized agents coordinating on tasks.