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Kep's RAG system enforces data ownership before LLM access

By PulseAugur Editorial · [1 sources] ·

Kep's retrieval-augmented generation (RAG) system prioritizes data ownership over model trust, ensuring that an LLM only accesses documents explicitly owned or shared with the user. This approach prevents inadvertent training data leakage and hallucinated citations, crucial for regulated industries. The system uses Flask and SQLite, with a strict WHERE clause in SQL queries to enforce permissions at the retrieval stage, ensuring data never leaves the user's local machine, such as a Mac Studio. AI

IMPACT This approach to RAG could set a new standard for data privacy in AI applications, particularly for industries with strict compliance requirements.

RANK_REASON The item describes a specific product's technical implementation and its unique approach to data security, rather than a novel AI model release or a significant industry-wide event.

Read on dev.to — LLM tag →

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

Kep's RAG system enforces data ownership before LLM access

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The item describes a specific product's technical implementation and its unique approach to data security, rather than a novel AI model release or a significant industry-wide event.
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COVERAGE [1]

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

    How Kep’s Retrieval Works: Ownership-First, Model-Last

    <h1> How Kep’s Retrieval Works: Ownership-First, Model-Last </h1> <p>That’s the entire point. </p> <h2> The Security Model: No Trust in the Black Box </h2> <p>Kep’s retrieval-augmented generation (RAG) follows one non-negotiable rule: <strong>ownership determines accessibility be…

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