Measure your model’s citation muscle—Grounding Depth Index reveals factual anchoring strength, cuts hallucination risk, and boosts stakeholder confidence.
Grounding Depth Index (GDI) quantifies how thoroughly a generative model links its output to explicit, verifiable sources; a higher score signals deeper factual anchoring and lower risk of hallucination.
Grounding Depth Index (GDI) measures how extensively a generative model ties each claim, figure, or quotation to an explicit, verifiable source. Think of it as a citation density score: a higher GDI indicates that the output is backed by more granular references—page numbers, dataset IDs, URL fragments—rather than a single broad citation. Because the metric is quantitative (often 0–1 or 0–100), teams can track factual anchoring over time and compare models or prompt versions.
Generative Engine Optimization (GEO) focuses on making AI-written content both discoverable and trustworthy. Search engines increasingly weigh source transparency when ranking AI-generated answers, and users punish hallucinations with abandoned sessions and brand distrust. A robust GDI score correlates with:
An intermediate team can automate steps 1–3 with NLP libraries (spaCy for statement detection, BM25 or embedding search for matching) and then layer light human review.
A high GDI signals that the model’s statements are tightly linked to explicit, verifiable sources—scholarly papers, government datasets, or other primary references—rather than surface-level summaries or second-hand blogs. In practice, that depth translates to fewer hallucinations, easier fact-checking, and stronger E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) signals for search engines. In GEO, those qualities raise the likelihood that the content is indexed, ranked, and retained by search algorithms because it can be traced back to authoritative evidence.
Primary-source-backed claims = 12. Total claims = 18. GDI = 12 ÷ 18 ≈ 0.67. Interpretation: Roughly two-thirds of the claims are firmly grounded. That’s decent for a draft, but the remaining one-third either rely on weaker secondary sources or no sources at all, which could undermine ranking potential and user trust. You would flag the uncited or weakly cited statements for verification or replacement with primary data.
1) Replace vague attributions (e.g., “industry reports”) with direct citations to the specific PDF, cage code, or DOI link. This deepens the grounding, boosting GDI and signaling higher content reliability to both users and search crawlers. 2) Embed structured data (e.g., Schema.org ‘Citation’ markup) around each source. This not only increases GDI by formalizing the link between claim and evidence, but also helps search engines parse and validate those connections, improving rich-result eligibility and crawl efficiency.
Narrative pieces often prioritize storytelling over citation, weaving insights without stopping for inline references, which naturally lowers GDI. A technical white paper, by contrast, is expected to list data tables, citations, and appendices—pushing its GDI upward. To balance creativity with grounding, intersperse the narrative with sidebars or footnotes linking to underlying data, and use contextual anchor text (e.g., “According to the FTC’s 2023 report…”) so the story flows while still providing verifiable touchpoints. The result: engaging prose that doesn’t sacrifice search visibility or factual integrity.
✅ Better approach: Set a hard cap on sources per section (e.g., 3–5), vet each reference for direct relevance, and prioritize peer-reviewed or first-party data. Automate a relevance check that flags any citation whose anchor text doesn’t appear in the surrounding 40-word window.
✅ Better approach: Tie GDI targets to page goals: informational pages can aim for a higher GDI, while product pages may prioritize clarity over depth. Review analytics monthly to correlate GDI with time-on-page and conversions, then adjust thresholds accordingly.
✅ Better approach: Implement a source-age limit (e.g., auto-flag anything older than 24 months in rapidly evolving niches) and maintain a vetted source whitelist. Schedule quarterly audits to replace outdated references before regeneration cycles.
✅ Better approach: Add an automated GDI check to the build process that blocks deployment if the score falls below a defined baseline. Send daily reports to the SEO team, and require a remediation pull request to restore the score before shipping.
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