21 Peer reviewed papers that support the Synthetic Users thesis
Here is a compilation of all the papers that help make a case for Synthetic Users.
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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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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