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Relevance 9/10Prompting and EvaluationIntermediate6 min read
Fact-Checking for LLM Evaluation
Fact-checking verifies whether model claims are supported by trusted context or references.
Why it matters for annotators
Factuality is a primary KPI in many production LLM systems.
Visual mental model
Claim -> verify source -> supported/unsupported/uncertain.
Examples (bad vs good)
Scenario: Real annotation scenario involving Fact-Checking for LLM Evaluation
Bad: Labeling quickly without applying project rubric.
Good: Applying rubric criteria, documenting rationale, and escalating uncertainty.
Common mistakes
- Skipping guideline details for edge cases.
- Applying inconsistent criteria across similar samples.
- Avoiding escalation even when uncertain.
Submission checklist
- Read the latest guideline update before each batch.
- Apply rubric dimensions explicitly in each decision.
- Escalate ambiguous items with concise rationale.