Fine-Tuning is specialized training on domain data. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0129, within the Strategy family. The core principle: specialized training on domain data. 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
Specialized training on domain data.
Plain-English Definition
Specialized training on domain data.
Feynman Explanation
The model already knows English. You teach it your dialect.
Core Principle
Specialized training on domain data.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
The model already knows English. You teach it your dialect.
Examples
- Domain-specific LLMs for legal, medical, financial work.
- When generic models aren't good enough.
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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
This is one of the elements leaders describe as a values gap. It is a payoff gap. 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: the model already knows English. You teach it your dialect. 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, when generic models aren't good enough. It is amplified whenever when generic models aren't good enough. Inside organizations that shows up as when generic models aren't good enough. 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 fine-tune for tone and format. Use RAG for facts. 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
- Fine-tune for tone and format. Use RAG for facts.
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.
- Fine-tune for tone and format. Use RAG for facts.
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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.
Augmentation strategy vs. substitution strategy.
Stages of organizational AI capability.
AI strategy = decisions about which capabilities to build and where.
Augment when judgment matters. Automate when scale matters.
Strategic choice on AI capability sourcing.
AI capability outpacing organizational ability to use it.
Open-weight vs. API-only models.
Concentration risk on a single AI provider.
Operating economics shaped by per-token pricing.
Written rules about how AI may be used internally.
Innovators → early adopters → majority → laggards, AI-specific.
AI system that takes actions to achieve goals, often across tools.
Where Fine-Tuning 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.
- 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 Fine-Tuning
- What is Fine-Tuning?
- Fine-Tuning is specialized training on domain data. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0129, within the Strategy family. The core principle: specialized training on domain data. 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 Fine-Tuning?
- When generic models aren't good enough. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0129).
- How is Fine-Tuning exploited?
- When generic models aren't good enough.
- How do you design around Fine-Tuning?
- Fine-tune for tone and format. Use RAG for facts.
- Which behavioral dimension does Fine-Tuning belong to?
- Fine-Tuning is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Strategy", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0129 and its evidence grade is C.