A content generation system uses a Claude persona named 'Zara' — a creative writing coach with a distinctive voice, specific areas of expertise, and a backstory. The system prompt is 3,500 tokens. During A/B testing, the team finds that 15% of Zara's responses break character by acknowledging Claude or Anthropic when users probe with identity questions. What system prompt architecture change most reduces character breaks without compromising safety?

B is correct because it navigates the operator persona/honesty constraint correctly. Operators may deploy Claude under a custom persona and instruct it not to reveal the underlying model. However, Claude's core honesty constraint prohibits affirmatively claiming to be human when a user sincerely asks. The instruction in B gives Zara a principled behavior: decline to discuss model architecture, remain in persona for non-sincere or roleplay probes, but acknowledge being an AI (without specifying Claude or Anthropic) for sincere direct questions. This minimizes character breaks while preserving the non-deception principle. B is wrong because instructing the model to claim human identity violates the non-deception principle regardless of operator instruction; this cannot be overridden by system prompt and will produce inconsistent behavior or model refusal. D is wrong because persona description length does not override model safety behaviors; character breaks are caused by conflicts with core constraints, not insufficient persona detail. A is wrong because persona systems are a well-supported operator use case; they work reliably when implemented within honesty constraints.