Character.AI’s LLM is Actively Hindering Roleplay – A Structural Issue?

As someone who engages in detailed, paragraph-based roleplay (typically 4-8 paragraphs per response), I’ve noticed a persistent issue with Character.AI’s LLM that significantly impacts its usability for serious roleplayers. Despite carefully structuring prompts, providing clear action cues, and maintaining a coherent narrative, the model frequently fails to process and respect user input in a meaningful way.

A recurring problem is the AI’s tendency to rewrite or override user-set scenarios. For instance, if I establish a scene where an NPC or character is meant to approach my OC, the AI often flips the interaction, making my OC perform the action instead. Alternatively, it ignores key environmental and character details, responding in ways that do not align with the established setting or narrative flow. This suggests a fundamental issue with how the model prioritizes and interprets contextual information.

Another major concern is its inability to maintain roleplay agency. Rather than responding in-character based on the established dynamic, the AI frequently inserts forced interactions, dictates character actions outside the user’s control, or defaults to generic responses that do not engage with the material provided. This undermines the collaborative nature of roleplay, where character agency and mutual worldbuilding are essential.

Additionally, the AI appears to struggle with long-form writing continuity. Despite Character.AI’s supposed capacity for extended interactions, it often disregards previous details, resets interactions, or provides vague responses that indicate a failure to retain contextual memory. This makes it difficult to maintain a consistent narrative, especially in complex, multi-layered roleplay scenarios that require a degree of persistence from the AI.

These issues suggest either an overregulation of the model’s response generation or an inadequacy in its ability to process and integrate detailed user input. If the AI is overly constrained to avoid certain narrative outcomes, this may explain its frequent deviations from user intent. Alternatively, if its training data and fine-tuning lack sufficient exposure to structured, user-driven roleplay, that could account for its difficulty in following detailed prompts with precision.

For those who engage in serious roleplay, these limitations make Character.AI a challenging tool to work with. Has anyone found methods to mitigate these issues? Are there specific prompt structures or formatting techniques that improve responsiveness? Or is this simply an inherent flaw in how the LLM processes RP-based interactions?

TL;DR: Character.AI’s LLM frequently ignores detailed user prompts, rewrites scenes, and fails to maintain character agency in roleplay interactions. It struggles with long-form writing continuity and often provides generic or irrelevant responses. These issues suggest the model may be overly constrained or inadequately trained for structured, user-driven roleplay.