hallucination-detector

To detect hallucinations in AI generated text, you trace claims back to source materials and validate whether fetched information actually supports the conclusions. This process involves claim extraction, source mapping, and provenance reporting.

Provenance tracking for fact-checking is the process of linking each claim to verifiable sources and assessing its grounding. It generates a structured report indicating source locations and confidence levels to validate response accuracy.

You verify if a summarization is grounded in cited sources by running a hallucination check that maps each extracted claim to its referenced document. This identifies unbacked assertions and provides remediation steps.

Yes, you can identify unbacked assertions in QA workflows by performing gap analysis on the response. This evaluates every claim against source materials to find ungrounded information and provide remediation steps.

The best way to trace claims to source documents is using provenance tracing to link each assertion to verifiable sources. This assesses grounding and generates a confidence report for content generation workflows.

Your AI response contains information not in the source material due to hallucinations. Running a hallucination check performs gap analysis to identify these unbacked assertions and provides remediation steps.