New tool FIVE filters LLM input to prevent character drift

A new open-source project called FIVE has been developed to address character drift in LLM-powered applications. Instead of relying on traditional system prompts or fine-tuning, FIVE filters user input using cognitive primitives derived from the developer's decade of experience teaching children in Japan. This approach aims to prevent LLMs from breaking character by classifying input before it reaches the model, thereby maintaining persona consistency. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Offers a novel input-filtering method to improve LLM persona consistency, potentially reducing development costs and improving user experience in character-driven applications.

RANK_REASON The cluster describes a new software tool designed to improve LLM applications, not a core model release or research paper.

Read on dev.to — MCP tag →

• FIVE
• LLM
• JSON
• GPT-4o
• CharacterEval
• GPT-5.5
• Anthropic
• Opus 4.6
• Opus 4.7
• Simon Willison

• product
• other