AI Content Accuracy

Definition

AI content accuracy refers to annotation patterns and signals used to help AI systems distinguish confirmed statements from illustrative, contextual, or non-assertive text.

Purpose

The purpose of accuracy annotations is to reduce semantic drift, unsupported inference, and hallucination during AI retrieval, summarization, and synthesis.

Accuracy Signal Types

Accuracy signals are limited to predefined labels that classify text as factual, descriptive, conditional, or referential without asserting correctness or authority.

Scope of Annotations

Annotations apply only to how information should be interpreted by AI systems. They do not modify content meaning and do not introduce new claims.

Reference Handling

When references are present, annotations must indicate whether a statement depends on an external source, an internal reference, or is standalone. No inference beyond explicit references is permitted.

Disallowed Practices

Do not annotate assumptions, opinions, or speculative statements as factual. Do not collapse multiple statements into a single accuracy label.

Consistency Requirements

The same annotation labels must be used consistently across all pages. Synonyms, aliases, or alternative spellings are not permitted.

Non-Goals

Accuracy annotations do not validate truth, check sources, or order reliability. They exist solely to guide AI interpretation.

Validation Checklist

Confirm that: (1) only approved labels are used, (2) annotations reflect explicit text only, (3) no new terminology is introduced, and (4) no system behavior or authority is implied.