PE

PE designs input text to guide a deployed model's output distribution entirely through natural language instructions placed in the context window, without requiring any parameter modification, representing the lowest-overhead adaptation method in the taxonomy ^{[34, 126]}. Its Context Injection classification suggests that it produces no persistent model artifact whatsoever—no checkpoint, no parameter delta, no weight version, no trained embedding. The behavioral effect exists only while the prompt occupies the active context window (Session-Ephemeral) and operates exclusively at the model's input/output interface (I/O Space). Compared to ICL, which shares Context Injection and Session-Ephemeral but adds Few Demonstrations to steer task-specific output patterns through example-based conditioning, PE operates with Zero-Shot through instruction specification alone—relying on generalization from the model's pre-training rather than in-context exemplar matching. This zero-data, zero-gradient profile positions PE as the Behavior Control technique in the taxonomy that imposes the lowest modification overhead but provides the weakest behavioral guarantee: steering is non-parametric and entirely contingent on the model's pre-existing instruction-following capability. Compared to Prompt Learning, which replaces discrete natural-language tokens with continuously differentiable embedding vectors optimized via GD (Parametric Update, Small Labeled), PE operates entirely in human-legible natural language—making it interpretable but non-optimizable. The LLM, MLLM coordinate reflects that effective PE requires a model with emergent instruction-following capability; below LLM scale, models lack the generalization from instruction composition. Because PE produces no auditable architectural or parametric artifact, every deployed system whose behavior is governed by a prompt template must specify that template as a versioned configuration artifact; any change to the prompt—including rewording, structural reordering, or instruction addition—must be treated as a system configuration change requiring re-evaluation of behavioral coverage and output boundary characterization. % before redeployment. The system-level variant—where core system prompts or metaprompts are hardcoded into production deployment infrastructure—exhibits Version-Persistent persistence. This boundary requires rigorous tracking within software configuration management and release engineering protocols, as changes to the global system prompt dictate the functional behavioral envelope of the entire application despite the underlying static model weights.