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Student builds Japanese PDF RAG demo with source citation

By PulseAugur Editorial · [1 sources] ·

Kohei Matsunobe, a student at ZEN University, developed a Retrieval-Augmented Generation (RAG) demo that allows users to upload Japanese PDFs and ask questions. The system provides answers with citations, indicating the specific file and page number the information was drawn from. Matsunobe prioritized citation accuracy by implementing custom Japanese-aware chunking that prevents chunks from spanning across page boundaries and by instructing the Gemini model to only use provided context and state "I don't know" if the answer is not found. AI

IMPACT Demonstrates a method for improving citation accuracy in RAG systems, crucial for applications requiring verifiable information.

RANK_REASON The item describes a demonstration of a RAG system built by a student, focusing on its specific implementation details rather than a novel model release or significant industry trend.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Student builds Japanese PDF RAG demo with source citation

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52 / 100
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Tool
The item describes a demonstration of a RAG system built by a student, focusing on its specific implementation details rather than a novel model release or significant industry trend.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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product, other
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · kohei matsunobu ·

    I Built a Japanese PDF RAG Demo Where You Can See What the Model Looked At

    <p><em>This article was originally published in Japanese on <a href="https://zenn.dev/koheip/articles/rag-visible-sources" rel="noopener noreferrer">Zenn</a>.</em></p> <h2> Introduction </h2> <p>Hi, I'm Kohei Matsunobe, a second-year student in the online (correspondence) program…

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