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.
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