November 22, 2024
Generative Agent Simulations of 1,000 People
A paper that thoroughly executes a parity study between Synthetic and Organic users.
What is RAG and why it’s important for Synthetic Research
Ahead of our RAG launch we explain Retrieval-Augmented Generation (RAG) and how it enhances Synthetic Users by providing increased realism, contextual depth, and adaptive learning, with profound implications for market research, user experience testing, training, education, and innovative product development.
Imagine Retrieval-Augmented Generation (RAG) as an advanced AI system that’s a bit like a hybrid between a creative writer and a savvy researcher. It’s built from two main parts: the large AI model that can chat about a wide range of topics because it contains the whole of the public internet (think of it as the creative writer) and a super-efficient information lookup system (our savvy researcher).
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This setup allows RAG to dig into a huge pool of knowledge - like having access to an enormous digital library - to find information that's not just on-topic but also detailed and accurate, much like a knowledgeable friend who always has the facts to back up their stories. So, when you ask RAG a question, it gives you an answer that’s both informative and interesting, blending the best of creativity with the precision of real-world information.
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Enhancing Synthetic Users with RAG:
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Synthetic Users, powered by standard LLMs, have pushed the boundaries in simulating user interactions. However, their responses can sometimes lack the depth or specificity that comes from real-life experiences and knowledge. Integrating RAG transforms these interactions by:
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- Increased Realism: RAG enables Synthetic Users to access a broader range of real-world knowledge, making their responses more authentic and varied.
- Contextual Depth: By retrieving information related to the specific context of an interview question (you define this), RAG enhances the relevance and depth of Synthetic User responses. In other words, the responses will be more relevant to your context.
- Adaptive Learning: As RAG models can pull from recent and up-to-date sources, they ensure that Synthetic Users' responses evolve with current trends and information.
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How much better is it?
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We compared the same Synthetic Interviews with Synthetic Interviews enriched with RAG and found that the latter provide more depth and detail on the personal stories — something that had been lacking from the synthetic / organic parity studies we had performed without RAG. The improvements are obvious and notable in the Depth and Specificity of insights and Comprehensiveness of coverage.
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Real-World Applications:
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The implications of RAG-enhanced Synthetic Users are profound across various industries:
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- Market Research: Our clients can now conduct more nuanced and informative user interviews, obtaining insights that further mimic those from real consumer feedback.
- User Experience Testing: Designers and developers can iterate on products and services with feedback that feels genuinely user-driven, accelerating the design process but also anchoring it in reality.
- Training and Education: RAG can simulate diverse user interactions for training customer service and sales teams, providing a range of scenarios that prepare employees for real-life encounters.
- Innovative Product Development: RAG-enhanced Synthetic Users revolutionize product development by providing realistic feedback for testing new ideas. This allows for accurate predictions of market trends and user acceptance, ensuring innovations meet real user needs.
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