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LLM governance engine adds RAGAS faithfulness scoring to combat hallucinations

By PulseAugur Editorial · [1 sources] ·

A developer has enhanced an LLM governance engine by integrating RAGAS faithfulness scoring, which measures how well a model's response aligns with provided context. This new feature complements the existing PII firewall, creating a two-stage enforcement process. The system now checks for sensitive data before a query reaches a model and verifies the accuracy of the model's output against the source material afterward. This aims to combat hallucinations, where models confidently state information not present in the context. AI

IMPACT Enhances LLM reliability by providing a mechanism to detect and flag responses that are not grounded in provided context, thus reducing the impact of hallucinations.

RANK_REASON The item describes an enhancement to an existing software tool, adding new features for LLM governance.

Read on dev.to — LLM tag →

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

LLM governance engine adds RAGAS faithfulness scoring to combat hallucinations

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The item describes an enhancement to an existing software tool, adding new features for LLM governance.
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High
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47 days old
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COVERAGE [1]

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

    Adding a Lie Detector to My LLM Governance Engine (RAGAS Faithfulness Scoring)

    <p>I ran the same refund-policy question through two models side by side, with the actual return policy pasted in as context. The local Llama model answered directly and scored 75% faithful to the source text. GPT-4o, given the exact same context, refused to answer at all — "I ca…

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