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58 lines
2.8 KiB
TeX
58 lines
2.8 KiB
TeX
% Hanzo Search: AI-Powered Search with RAG
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\documentclass[11pt,twocolumn]{article}
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\usepackage{../shared/hanzocover}
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\usepackage{amsmath, amssymb, amsthm}
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\usepackage{mathtools}
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\usepackage{bm}
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\usepackage{graphicx}
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\usepackage{booktabs}
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\usepackage{hyperref}
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\usepackage{enumitem}
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\usepackage{algorithm}
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\usepackage{algpseudocode}
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\usepackage{xcolor}
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\usepackage{float}
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\usepackage{subcaption}
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\usepackage{multirow}
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\usepackage{listings}
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\input{../shared/lstlang}
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\usepackage[numbers]{natbib}
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\hypersetup{colorlinks=true,linkcolor=black,citecolor=blue,urlcolor=blue}
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\newtheorem{theorem}{Theorem}
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\newtheorem{lemma}[theorem]{Lemma}
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\newtheorem{definition}{Definition}
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\newtheorem{proposition}[theorem]{Proposition}
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\title{Hanzo Search: AI-Powered Search with Retrieval-Augmented Generation}
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\author{
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Marcus Chen \quad
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David Wei \quad
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Zach Kelling \\
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\textit{Hanzo AI Research} \\
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\texttt{[email protected]}
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}
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\date{September 2025}
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\begin{document}
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\hanzocoverpage
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\begin{abstract}
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We present \textbf{Hanzo Search}, an AI-powered search engine that combines retrieval-augmented generation (RAG) with generative user interfaces to deliver synthesized, source-grounded answers instead of traditional ranked document lists. Hanzo Search introduces three key innovations: (i) a \emph{hybrid retrieval pipeline} that fuses sparse (BM25) and dense (neural embedding) retrieval with learned fusion weights, achieving 18.7\% higher NDCG@10 than either method alone on the BEIR benchmark; (ii) a \emph{generative UI framework} that dynamically renders structured answer components (tables, charts, comparisons, timelines, code blocks) based on query intent classification, increasing user engagement by 34\% compared to text-only answers; and (iii) a \emph{source attribution system} that provides sentence-level provenance for every claim in the generated response, achieving 94.3\% attribution accuracy on our SourceVerify benchmark. We evaluate Hanzo Search on standard information retrieval benchmarks (MS MARCO, BEIR, Natural Questions) and a novel generative search benchmark (GenSearch-500), demonstrating state-of-the-art performance in answer quality, factual accuracy, and user satisfaction. Production deployment analysis from 12 months of operation with 2.1 million queries reveals that generative search reduces the average number of follow-up queries by 47\% and increases task completion rates by 28\% compared to traditional search interfaces.
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\end{abstract}
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\input{sections/01-introduction}
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\input{sections/02-system-architecture}
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\input{sections/03-query-understanding}
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\input{sections/04-hybrid-retrieval}
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\input{sections/05-grounded-generation}
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\input{sections/06-generative-ui}
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\input{sections/07-evaluation}
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\input{sections/08-production-deployment}
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\input{sections/09-related-work}
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\input{sections/10-discussion}
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\input{sections/99-conclusion}
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\end{document}
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