Retrieval-Augmented Generation (RAG) is generate based on retrieved documents, not just trained weights. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0236, within the Workflow family. The core principle: generate based on retrieved documents, not just trained weights. 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
Generate based on retrieved documents, not just trained weights.
Plain-English Definition
Generate based on retrieved documents, not just trained weights.
Feynman Explanation
Memory the model didn't have to memorize.
Core Principle
Generate based on retrieved documents, not just trained weights.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Memory the model didn't have to memorize.
Examples
- Enterprise chatbots grounded in internal docs.
- Standard pattern for enterprise LLM deployment.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
When this element shows up in a diagnostic, the instinct is to train people out of it. Training rarely moves it. 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: memory the model didn't have to memorize. 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, standard pattern for enterprise LLM deployment. It is amplified whenever standard pattern for enterprise LLM deployment. Inside organizations that shows up as standard pattern for enterprise LLM deployment. 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 quality of retrieval matters more than quality of generation. The leverage is not in explaining the behavior to people. It is in changing what the behavior earns.
Famous Experiments
Pending editorial review.
Design Principles
- Quality of retrieval matters more than quality of generation.
Measurement Approaches
Pending editorial review.
Evidence
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The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Quality of retrieval matters more than quality of generation.
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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.
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.
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.
Where Retrieval-Augmented Generation (RAG) 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 Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
Questions about Retrieval-Augmented Generation (RAG)
- What is Retrieval-Augmented Generation (RAG)?
- Retrieval-Augmented Generation (RAG) is generate based on retrieved documents, not just trained weights. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0236, within the Workflow family. The core principle: generate based on retrieved documents, not just trained weights. 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 Retrieval-Augmented Generation (RAG)?
- Standard pattern for enterprise LLM deployment. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0236).
- How is Retrieval-Augmented Generation (RAG) exploited?
- Standard pattern for enterprise LLM deployment.
- How do you design around Retrieval-Augmented Generation (RAG)?
- Quality of retrieval matters more than quality of generation.
- Which behavioral dimension does Retrieval-Augmented Generation (RAG) belong to?
- Retrieval-Augmented Generation (RAG) is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Workflow", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0236 and its evidence grade is C.