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Concept Processing Prompts: Engineering a Universal Foundation for Any AI Novel

Griffin Chesnik
13 min read11 hours ago

Introduction: The Foundation Layer for Any Narrative

Photo by Berke Citak on Unsplash

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In our hierarchical planning framework for AI novel generation, concept processing is the foundational layer upon which all subsequent consistency depends. Like architectural blueprints, this initial phase establishes the parameters, boundaries, and structural elements that will support any narrative, regardless of the initial premise provided by the user.

The power of well-crafted concept processing prompts lies in their versatility — they must be universal enough to work with any user-provided concept while specific enough to create meaningful guardrails for consistency. When executed properly, this initial phase prevents major consistency issues across any genre or story type by establishing clear rules and boundaries tailored to the specific narrative.

This article provides a comprehensive examination of universal concept processing prompts designed to work with any user premise, from space opera to historical romance to contemporary thriller.

Universal Architecture for Concept Processing

The concept processing phase accomplishes five essential functions, regardless of the specific narrative:

  1. Transforms any user-provided premise into structured parameters that guide all subsequent planning
  2. Establishes clear boundaries customized to the specific narrative concept
  3. Creates consistency guardrails for genre, theme, setting, and scope
  4. Sets a foundation for character development appropriate to the specific story
  5. Converts abstract creative concepts into concrete, machine-actionable guidelines

The prompts in this phase must be general enough to apply to any premise while producing outputs specific to the particular narrative. Let’s examine each prompt in detail.

Prompt 1: Initial Concept Expansion (Universal Design)

Purpose

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Griffin Chesnik

Written by Griffin Chesnik

ML Engineer exploring AI frontiers: RAG systems, LLMs & enterprise solutions. Building innovative applications documenting techniques to overcome LLM limits

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