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New library offers explainable RAG confidence scoring

By PulseAugur Editorial · [1 sources] ·

A new TypeScript library called `transparent-confidence` has been developed to address the challenge of assessing the trustworthiness of answers generated by retrieval-augmented generation (RAG) systems. Unlike raw cosine scores or uncalibrated LLM self-confidence, this library provides a structured scorecard at query time. It analyzes various signals such as retrieval scores, document metadata, and conflict indicators to generate a confidence score between 0 and 100, along with machine-readable warnings and recommended actions. AI

IMPACT Provides a standardized method for evaluating RAG answer quality, potentially improving user trust and system reliability.

RANK_REASON The cluster describes a new software library release.

Read on dev.to — LLM tag →

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

New library offers explainable RAG confidence scoring

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COVERAGE [1]

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

    Stop Serving Raw Cosine Scores: Explainable RAG Confidence Scoring at Query Time

    <h2> The gap nobody talks about </h2> <p>You've got a RAG pipeline. It retrieves documents, generates an answer, and hands it to your users. Somewhere between "retrieved 0.84 cosine similarity" and "show answer in UI" you've made a judgment call — usually implicit — about whether…

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