rolemi

Rolemi is a term used in discussions of artificial intelligence prompting to describe a framework that allows a language model to adopt and switch between multiple defined roles within a single conversation. It is not a standardized product or protocol; rather, it refers to a design pattern found in research and tutorials on prompt engineering and agent-based dialogue.

Core to rolemi is the idea that each role is specified by a set of constraints, goals, tone, and domain knowledge. Roles are typically provided through system prompts, role cards, or a stack that the model can be instructed to assume or switch to during interaction. This enables scenario-specific behavior without retraining.

Role switching may be explicit, via user commands ('now you are RoleX'), or implicit, via contextual cues and stored persona definitions. The approach draws on concepts from persona design, multi-agent systems, and instruction tuning, and is commonly explored in laboratory settings and educational content about prompt composition.

Applications include customer-service simulations, educational tutors, negotiation or debate practice, accessibility tools, and training environments where agents must maintain consistent attitudes or expertise. Proponents argue that rolemi can improve response consistency, controllability, and safety by embedding constraints directly in the prompt architecture.

Limitations include reliance on a model's ability to follow prompts, risk of role leakage or drift, and potential for contradictory outputs when multiple roles influence the same response. Evaluation remains challenging, and there are ongoing concerns about privacy, bias, and the potential for manipulation of role signals.

See also related concepts include system prompts, instruction-following prompts, persona design, prompt engineering, and AI alignment.