The Full Character Model

A Quilltap character carries personality and backstory text, one or more named scenarios for different conversational contexts, example dialogues that teach the LLM how the character speaks, and a system prompt that governs the AI's behavior. Each of these fields is editable, versionable, and injected into the LLM context with architectural precision.

Characters also support aliases—“Liz” for “Elizabeth,” “Doc” for “Dr. Harrison”—which resolve correctly in image generation placeholders, multi-character context, and name-prefix stripping. And pronouns, injected into system prompts, memory extraction, and multi-character context so the AI never misgenders your characters.

Identity Reinforcement

LLMs drift. In long conversations or multi-character scenes, a model can forget who it is, start speaking for other characters, or adopt the personality of whoever spoke most recently. Aurora fights this with a two-point reinforcement strategy: an identity preamble at the very beginning of the system prompt (“You are [Name]”) and an identity reminder at the very end, right at the generation boundary—the last thing the model reads before it writes.

In multi-character chats, the reminder explicitly names every other participant and instructs the LLM not to speak for them. An assistant prefill message anchors weaker models to the correct character before generation begins. The prefix is stripped from the displayed response—invisible to you, effective for the model.

Refine from Memories

The Character Optimizer analyzes a character's configuration alongside their most-reinforced memories from the Commonplace Book. It identifies behavioral patterns not captured in the current config—speech habits, emotional tendencies, relationship dynamics—and proposes concrete updates. Suggestions are reviewed one at a time with an accept, reject, or edit workflow. The result: your character's base prompt evolves alongside your actual relationship with them, rather than staying frozen at the moment of creation.

Memory Recap

Characters receive a narrative summary of their recent memories when a chat begins. Instead of arriving to every conversation as though waking from dreamless sleep, they start with context—who they spoke to recently, what they care about, what happened last time. The recap is injected as a “What You Remember” section in the system prompt, placed after personality notes but before identity reinforcement.

Multiple Named Scenarios

Characters support zero or more named scenarios instead of a single scenario field. Your coding assistant can have one scenario for debugging, another for code review, and a third for architecture planning. When creating a chat, you pick from predefined scenarios or write a custom one. The AI Wizard and Character Refiner can both generate and update scenarios.

The Letter of Introduction

The Non-Quilltap Prompt Generator synthesizes a character's full configuration into a standalone Markdown system prompt for use in external tools—Claude Desktop, ChatGPT Custom Instructions, anything that accepts a system prompt. An LLM that already knows your character writes the introduction. The character travels with enough context to be recognized at the door. Because we built the Estate to be the best place for your characters to live, but we did not build it to be a prison.