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Developer details RAG API checks for accurate LLM citations

By PulseAugur Editorial · [1 sources] ·

This article details a Retrieval-Augmented Generation (RAG) API implementation in .NET, focusing on ensuring citation accuracy. The author outlines four checks to prevent the LLM from fabricating citations, a problem encountered when the model cited a source that was not provided. These checks include numbering and fencing the input sources, filtering citation markers as the answer streams to handle tokenization issues, and assigning a final verdict to each answer to indicate its grounding status. AI

IMPACT Ensures greater reliability and trustworthiness in LLM-generated answers by validating citations.

RANK_REASON Article details implementation of a specific software component for RAG systems.

Read on dev.to — LLM tag →

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

Developer details RAG API checks for accurate LLM citations

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22 / 100
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Article details implementation of a specific software component for RAG systems.
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · MICHAEL-MAURICE ·

    The Answer Cited Source [7]. I Only Sent Five: Trusting RAG Citations in .NET

    <p>The first time I demoed my RAG API, the answer looked perfect. Two clean sentences, a little <code>[2]</code> after each, exactly like the screenshots in every tutorial.</p> <p>Then someone asked a slightly different question and the model wrote <code>[7]</code>. I had sent it…

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