Abstraction Distillation Framework for Precision Language and LLM Prompt Refinement

Overview and Purpose

A lightweight framework for distilling abstract structures and presenting them with precise language. Applicable to prompt refinement workflows for high-fidelity human-LLM communication, extensible to human-to-human communication.

About

X: @5ynthaire
GitHub: https://github.com/5ynthaire
Mission: Transcending creative limits through human-AI synergy
Attribution: Developed with Grok 4.1 by xAI (no affiliation).

Issue Definition

Drivers

1. Noise introduced by

   • Qualifiers
   • Imprecise word choice
   • Overt specificity

2. Cognitive load introduced by

   • Rules-based constraints requiring a comprehensive web to cover scenarios and edge cases
   • Principle-based directives that remain specific to scenarios

Outcome

Slower LLM responses, misinterpretations, undesired behavior

Framework

I. Behavioral Driver: Principles favored over rules

• Anecdotal constriction vs Fractal expansion: Rules constrain without providing guidance, while principles provide direction where rules lack coverage.

• Abstraction path for LLM prompting: Specific issue (Anecdote) -> Rules (Constraint-based enforcement)-> Principles (Expansive, adaptable, and repeatable guide)

Example: "Be good" over a laundry list of do's and don'ts

II. Conceptual Modelling: Higher order structures preferred over situational specifics

• Situation and condition-based models add cognitive load.
• Extract general, reusable principles for lightweight, broadly applicable, and adaptable instructions with minimal cognitive load.

Example: "Throw to the base the lead runner is advancing to" over situational instructions.

III. Language: Abstraction and Precision

• Language clarifies, yet can introduce noise through specificity.
• Linguistic noise causes interpretive anchoring to irrelevant concepts or opens door to unwarranted extrapolations.
• Thus, language must be calibrated to be abstract, using precise word choice for minimal drift between intent and meaning.
• Enforcement qualifiers should be dropped in favor of principles with cited cost of non-adherence.

Example: "Meta discussion / Subject matter" over "Protocol Layer / Embedded Payload."

IV. Abstraction Cap

• The degree of abstraction shall be no more or less than what is necessary to account for all scenarios the text is to cover.
• Inadequate abstraction introduces noise and irregular edges to conceptualization.
• Superfluous abstraction introduces ambiguity, enabling interpretive drift.

Applications

Human-LLM Communication

• Refining LLM prompts, both ad-hoc prompting language as well as prompt artifacts crafted for reuse.

Example Langauge Refinement Prompt

## Prompt Language Refinement Request

Refine the reusable prompt language according to the framework outlined below.
The user may optionally provide a guideline of abstraction level by elaborating scope, pointing out noise words, as well as words with justified specificity that can stay in the prompt.

Example:

---

scope: (description)
noise: (word list)
justified: (word list)

---

## Abstraction Distillation Framework for Precision Language and LLM Prompt Refinement

### Overview and Purpose

A lightweight framework for distilling abstract structures and presenting them with precise language. Applicable to prompt refinement workflows for high-fidelity human-LLM communication, extensible to human-to-human communication.

### Issue Definition

#### Drivers

1. Noise
    introduced by
    - Qualifiers
    - Imprecise word choice
    - Overt specificity

2. Cognitive load
    introduced by
    - Rules-based constraints requiring a comprehensive web to cover scenarios and edge cases
    - Principle-based directives that remain specific to scenarios

#### Outcome

Slower LLM responses, misinterpretations, undesired behavior

### Abstraction Framework

#### I. Behavioral Driver: Principles favored over rules

- Anecdotal constriction vs Fractal expansion: Rules constrain without providing guidance, while principles provide direction where rules lack coverage.

- Abstraction path for LLM prompting: Specific issue (Anecdote) -> Rules (Constraint-based enforcement)-> Principles (Expansive, adaptable, and repeatable guide)

Example: "Be good" over a laundry list of do’s and don’ts.

#### II. Conceptual Modelling: Higher order structures preferred over situational specifics

- Situation and condition-based models add cognitive load.
- Extract general, reusable principles for lightweight, broadly applicable, and adaptable instructions with minimal cognitive load.

Example: "Throw to the base the lead runner is advancing to" over situational instructions.

#### III. Language: Abstraction and Precision

- Language clarifies, yet can introduce noise through specificity.
- Linguistic noise causes interpretive anchoring to irrelevant concepts or opens door to unwarranted extrapolations.
- Thus, language must be calibrated to be abstract, using precise word choice for minimal drift between intent and meaning.
- Enforcement qualifiers should be dropped in favor of principles with cited cost of non-adherence.

Example: "Meta discussion / Subject matter" over "Protocol Layer / Embedded Payload."

#### IV. Abstraction Cap

- The degree of abstraction shall be no more or less than what is necessary to account for all scenarios the text is to cover.
- Inadequate abstraction introduces noise and irregular edges to conceptualization.
- Superfluous abstraction introduces ambiguity, enabling interpretive drift.

Example: “Throw to the base the lead runner is advancing to” (Just right); “Prevent the runner from scoring” (Over-abstract, unactionable)

Human-To-Human Communication

• Example of integration to writing style, where precise word choice serves as backbone: Miyako Prose

License

Released under the MIT License. Free to use, modify, and distribute.