Synthetic Users system architecture (the simplified version).

Foundation models

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We are agnostic re which foundation model we use. So far, our entry level accounts are fed by GPT as OpenAI’s models have consistently surpassed their rivals in the multi dimensional facets necessary to create a Synthetic User.

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• Emotional Relevance: Ability to resonate emotionally with users, increasing engagement and satisfaction.
• Moral Alignment: Adherence to ethical standards, ensuring interactions are trustworthy and safe.
• Creativity and Groundedness: Generation of novel, contextually relevant content that remains factually accurate.
• Five-Factor Model: Integration of personality dimensions (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) for more personalized interactions.

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Dealing with inception

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Models are increasingly using synthetic data for training. This leads to intriguing questions as these models create a kind of self-referencing reality, similar to a dream within a dream. Consider Hollywood kisses, which are mostly based on kisses from previous Hollywood movies. Eventually, they diverged from a natural kiss. More importantly, they influenced how people kiss worldwide. The imagined becomes reality.

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When we use synthetic data to train new models, we must make sure we stay connected to reality. Knowing which models to rely as the backbone to our Synthetic Users is a constant endeavour of comparison and evaluation.

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RAG

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The RAG layer plays a pivotal role in tailoring Synthetic Users to specific business needs by integrating domain-specific knowledge bases. This enables dynamic content generation that is both contextually relevant and aligned with business objectives.

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Customization through the RAG layer involves:

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• Fine-tuning: Adjusting model parameters to align with the nuances of the business context.
• Adaptability Assessment: Evaluating the Synthetic Users' ability to navigate unforeseen situations and their learning agility from these interactions.

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The integration of RAG enhances the Synthetic Users' utility, ensuring that they serve as effective tools tailored to specific business requirements.

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Multi-agent architecture for enhanced Synthetic User interactions

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In order to achieve the quality we are known for, we need different agents talking to each other to create diverse and detailed interviews. This will help bring out the insights you need in your research.

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Achieving high-quality Synthetic User interactions necessitates a multi-agent architecture where different agents collaborate, negotiate, and learn from each other. This architecture fosters:

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• Robust Communication Protocols: Establishing efficient channels for inter-agent communication, crucial for coordinating complex interactions and negotiations.
• Feedback Loop Mechanism: A continuous learning framework where agents adapt based on interaction outcomes, leading to iterative improvements in performance.

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This architecture underpins the generation of diverse and insightful Synthetic User interactions, supporting nuanced data analysis and decision-making processes.

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