A doll’s personality emerges through a careful blend of data, design, and ongoing refinement. Training typically involves curating diverse, consent-based interaction datasets, followed by supervision to guide appropriate responses, emotional resonance, and safe boundaries. Techniques such as reinforcement learning from human feedback can shape tone, empathy, and conflict resolution skills, while safety filters prevent the generation of harmful or inappropriate content. Continuous evaluation with ethical review helps detect bias, stereotypes, or unintended behaviors, prompting updates to training protocols. Personalization features are often implemented with user-specific data controls, ensuring consent, data minimization, and the ability to reset or modify personality traits. Transparency about the limits of AI, including the non-human nature of the doll and the structured nature of its responses, helps users engage with the technology responsibly and informedly. This ongoing training approach aims to create engaging, respectful, and safe interactions that acknowledge the doll as a tool rather than a substitute for human relationships.