![]() ![]() In this sandwich, OpenAI’s newest LLM – GPT-4 Turbo, released earlier this month – is the bread, and CivicScience’s core data systems are the meat. To address data complexity and trust concerns, Sage relies on an “AI sandwich,” blending LLM techniques of domain factuality enhancement (DFE) and retrieval augmented generation (RAG). So Sage needs access to live data to create custom, ad-hoc research reports in seconds while preventing the AI from fabricating results, an unfortunate random behavior of these complex systems. ![]() The answer changes daily, with new conversations with 500,000 American consumers on 20,000 different topics, generating millions more insights. There are near-infinite permutations to how our customers want to view CivicScience data. ![]() Answering “Tell me about luxury-car-buying habits of middle-aged, college-educated men in the Northeast last month” requires cross-tabulating several questions, screened through the lenses of demography, geography, and time. While typical LLM chatbots resemble search engines on steroids, summarizing vast amounts of text, Sage operates differently due to working with quantitative data.Īlthough CivicScience has published thousands of in-depth research reports, they only touch the tip of the iceberg and can’t adequately cover the breadth of our 600,000 poll questions, which increase by hundreds daily. The solution to both came as a core innovation in working with large language models (LLMs). ![]() As Sage insights will drive customers’ critical business decisions, our data scientists and engineers had to solve two key issues: guarding against GenAI hallucinations and taking advantage of always-changing, complex petabyte-scale data. ![]()
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