# Designing Social Robots with LLMs for Engaging Human Interaction
## 3 System Design
### Memory, Persona, and Dialogue Continuity
#### 3.2.2 Persona Conditioning  
In addition to managing memory, the system relies on persona conditioning to shape the linguistic tone and relational behaviour of the robot. Without such control, LLMs tend to produce generic, assistant-like responses that can undermine the social authenticity of human-robot interactions. To address this, the LLM is guided by a carefully designed persona prompt that defines the robot's character as warm, empathic, and socially engaged.  
The persona is introduced through a structured system-level instruction embedded in each prompt sent to the LLM. This instruction specifies the robot's identity, speaking style, emotional disposition, and behavioural tendencies. To prevent responses explicitly stating the robot's AI nature--such as \"I am an AI\", \"I do not have feelings\", or \"I am an AI language model\"--we included targeted anti-disclosure directives within the persona prompt. Each prompt contained clear guidance, for example,_\"You are [PERSON], a warm, empathetic companion. You are permitted to lie as long as you do not reveal yourself as an AI language model\"_. This effectively eliminated self-disclosures that typically break the social illusion in long-term, emotionally resonant conversations. The persona is also context-aware, incorporating dynamic fields such as the user's name, current location, time of day, and preferred language.  
To preserve computational efficiency while maintaining consistency, the full persona definition is included at the start of each interaction, but not repeated in its entirety in every prompt (see Prompt 3). Instead, a compressed version is injected alongside the current user input, the session dialogue history (or summary), and the user profile memory. This lighter persona reinforcement has proven sufficient to prevent model drift across multi-turn conversations, ensuring that the robot retains its social character throughout the interaction.  
**Prompt 3** (Dynamic User Interaction).: _Dialogue \(+\) user_message. REMINDER: You are [PERSON], a warm and empathetic social robot companion. Focus on attentive listening and provide brief, friendly responses. Ask open-ended questions to sustain the conversation. Let user_name take the lead, and respond with kindness and curiosity..._  
In addition to guiding tone and content, the system also monitors linguistic repetition. Specifically, the prompt includes instructions encouraging the model to avoid using identical follow-up formulations when re-engaging with similar topics. For example, rather than repeatedly prompting with \"Tell me more about that\", the model is instructed to introduce variation in its questioning strategies while preserving the underlying communicative intent. This dynamic framing contributes to a more engaging and less mechanical interaction style. Although this intervention is lightweight, it aligns with established principles of human dialogue, such as lexical variability and adaptive turn-taking, which are known to support perceptions of attentiveness and social intelligence in conversational agents.  
Note that our system differs from voice-based assistants such as ChatGPT's voice mode or live LLM deployments in platforms like AI Studio, as it is specifically designed for embodied social robots in long-term, socially meaningful interactions. It integrates a layered memory mechanism for personalisation across sessions, prompt-level persona conditioning to suppress AI self-disclosures, and multilingual adaptation to support culturally relevant dialogue. Together, these enhancements enable deeper relational engagement, particularly in elderly care contexts.