In-Context Learning is models learning from examples in the prompt. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0154, within the Workflow family. The core principle: models learning from examples in the prompt. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
Scientific Definition
Models learning from examples in the prompt.
Plain-English Definition
Models learning from examples in the prompt.
Feynman Explanation
Demonstration is faster than retraining.
Core Principle
Models learning from examples in the prompt.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Demonstration is faster than retraining.
Examples
- Few-shot prompting.
- Often the right answer before reaching for fine-tuning.
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Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
Executives usually notice this element only after it has cost something. By then it looks like a one-off. It is not. The mechanism underneath it operates in the Incentives dimension — what makes behavior more or less likely?. You can recognize it in the field by its signature: demonstration is faster than retraining. Every element in the Incentives dimension changes the perceived payoff of an action before the action happens, which is exactly where incentive design has leverage.
How it gets exploited
Left undesigned, often the right answer before reaching for fine-tuning. It is amplified whenever often the right answer before reaching for fine-tuning. Inside organizations that shows up as often the right answer before reaching for fine-tuning. The pattern is the same one Goodhart's Law describes: the measurable proxy attracts the effort, and the purpose behind it quietly loses funding.
How the Lab designs around it
The redesign move is to master prompt engineering before investing in fine-tuning. Watch for it at the boundaries: handoffs, promotions, incident reviews, and budget cycles are where this element gets its power.
Famous Experiments
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Design Principles
- Master prompt engineering before investing in fine-tuning.
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.
- Master prompt engineering before investing in fine-tuning.
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Pending editorial review.
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.
How much input the model can process at once.
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.
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.
Where In-Context Learning is cited in the corpus
Essays, field guides, and diagnostics from The Incentives Lab that apply this element.
- Field guideIncentives: definition, types, examples
The parent field guide for this element.
- ReferenceThe laws of incentives
Goodhart, Campbell, and the Cobra Effect.
- EssayAI Agents Inherit Your Incentives
How this element propagates into automated systems.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about In-Context Learning
- What is In-Context Learning?
- In-Context Learning is models learning from examples in the prompt. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0154, within the Workflow family. The core principle: models learning from examples in the prompt. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
- What is an example of In-Context Learning?
- Often the right answer before reaching for fine-tuning. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0154).
- How is In-Context Learning exploited?
- Often the right answer before reaching for fine-tuning.
- How do you design around In-Context Learning?
- Master prompt engineering before investing in fine-tuning.
- Which behavioral dimension does In-Context Learning belong to?
- In-Context Learning is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Workflow", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0154 and its evidence grade is C.