Context Resolution by Prompt Directive

Abstract

Context resolution by prompt directive is the process by which a language model alters or narrows its contextual frame in response to explicit user instructions embedded in the prompt. Unlike passive observation-based resolution, directive-based methods allow for immediate and precise control over model behavior, tone, memory scope, or role. This paper defines the mechanism, differentiates it from other forms of context management, outlines usage patterns, failure modes, and future extensions.

1. Definition

Context resolution by prompt directive refers to runtime context control in which the model responds to clearly embedded textual instructions. Examples include "Act as...", "Ignore previous", or "Only use the following...". These cause the model to reframe, reset, or narrow its active context.

2. Mechanism

Transformers maintain continuity through tokenized KV caches and long-range attention. Directives interrupt this flow by supplying tokens that carry semantic priority. This control is not architectural—it is behavioral, induced through trained responses to directive patterns.

3. Examples

• Role shift: "You are a Victorian-era physician" changes diction and assumptions
• Scope reduction: "Use only this passage" localizes attention
• Reset: "Forget prior text" signals context discard

4. Comparison

Directive-based resolution is explicit and user-driven. It shifts focus quickly and reliably. In contrast, observation-based resolution is implicit, model-driven, and more gradual.

5. Failure Modes

• Directive ambiguity: model blends instruction with prior memory
• Contradiction: e.g., "Ignore above" vs "Continue the story"
• Token decay: directive too early, overridden by later input

6. Applications

• Interactive chatbots
• Role-based simulations
• Thread control in document generation

7. Reinforcement Strategies

• Structural markers
• Repeating directives
• Localizing scope in prompt

8. Training Dependency

Models must be trained on directive-like structures to respond effectively. Instruction tuning improves this.

9. Hybridization

Directives and observation can work together, but directives override when both exist.

10. Synthesis

Prompt directives offer fine control over model behavior, making context shifts predictable and programmable.