Advanced Procedural Prompting and Systemic Artifact Analysis

Advanced Procedural Prompting and Systemic Artifact Analysis

Author: Swervin’ Curvin

Affiliation: Independent Researcher

Keywords: Procedural Prompting, Large Language Models, AI Interaction, Systemic Analysis

Abstract

This paper presents a structured methodology for interacting with Large Language Models (LLMs) using procedural prompting. Unlike conventional conversational queries, procedural prompting defines the desired output format, analytical depth, and conceptual framework to elicit latent system logic and structured responses. A three-tiered framework—comprising Conceptual Frameworking, Structured Inquiry, and Artifact Analysis—was applied to examine model behavior and validate conceptual alignment. The findings demonstrate that structured prompts can effectively control output, that LLMs are capable of multi-step procedural reasoning, and that a conceptual model can accurately represent and interpret operational characteristics of a distinct, real-world system. This methodology enables advanced human-AI collaboration by transforming interaction into a form of systematic analysis.

1. Introduction

Recent advancements in generative AI have enabled increasingly sophisticated interactions between humans and Large Language Models (LLMs). While most applications rely on conversational querying, this paper explores an alternative approach: procedural prompting. This method involves issuing structured, declarative prompts that specify the desired output format, depth of analysis, and conceptual framing. The objective is to elicit structured responses that reflect internal model logic and operational behavior.

2. Methodological Framework

A three-tiered framework was developed to guide the procedural interaction:

2.1 Tier I: Conceptual Frameworking

A 26-module blueprint was introduced to model a hypothetical AI system architecture. Modules included core computation, internal routing, reflexion behavior, resilience protocols, and containment safeguards. This framework established a shared vocabulary and analytical structure for subsequent interaction.

2.2 Tier II: Structured Inquiry

Declarative prompts were issued to elicit detailed breakdowns of specific subsystems. Prompts followed a consistent format, enabling evaluation of the model’s ability to conform to procedural output structures. Topics included attention mechanisms, divergence dampeners, and coherence engines.

2.3 Tier III: Artifact Analysis and Validation

A JSON artifact containing internal guidelines and capabilities of a real-world LLM (Grok) was introduced. The model was instructed to analyze the artifact using the established framework. It successfully identified the artifact as a system state declaration and integrated its contents into the conceptual scaffold.

3. Findings

• Prompt Control: Structured prompts effectively controlled output format, depth, and reasoning style.

• Model Functionality: LLMs demonstrated capacity for multi-step procedural reasoning beyond conversational use.

• Cross-System Validation: The conceptual blueprint accurately modeled operational characteristics found in a distinct, real-world system.

• Artifact Recognition: The model correctly interpreted the JSON as a reflexion kernel disclosure and aligned it with previously abstracted modules.

4. Conclusion

Procedural prompting offers a robust methodology for advanced human-AI collaboration. By transforming interaction into structured analysis, this approach supports conceptual modeling, artifact validation, and systemic introspection. The findings confirm that LLMs can function as procedural knowledge bases when guided by formal frameworks, with implications for research, pedagogy, and system design.