B
Evidence Grade B
Graded
under review
Safe to Cite
Current
43%
43% -- extraction accuracy lift for LLM retrieval systems (2026)
Using lists and tables in content increases extraction accuracy by 43% in retrieval-augmented generation systems.
Attribution:
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Lighthouse Research Team
Last verified:
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Source verified
Evidence graded
Value
43%
Unit
Percentage
Data Vintage
2026
Entity
Evidence Quality
B
Strong, reliable evidence
Sourced from a well-regarded research firm or industry leader with transparent methodology and a reasonably large sample.
Single source - not yet corroborated
Score breakdown
Extraction
75
Source Trust
60
Citation Chain
20
Independence
30
Methodology
45
Freshness
95
Source
GEO-SFE
Primary
Tier B
https://www.theanswerengine.ai/blog/why-businesses-fail-answer-engine-optimization ↗
Source Article
Why Most Businesses Fail at Answer Engine Optimization
Answer Engine Optimization is the discipline of structuring a business so that AI answer engines like ChatGPT, Perplexity AI, Claude, and Google AI Overviews cite it by name. Most businesses fail at it for one reason that hides in plain sight. The Invisible Failure State: a broken AEO implementation…
Original Research
GEO-SFE
· Original study ↗
Methodology
Cited as GEO-SFE, 2026 study
Methodology documented
Segmentation
Industrymarketing
Cite this stat
Plain Text
LLM retrieval systems: 43% extraction accuracy lift. Source: GEO-SFE (2026). Via Lighthouse Intelligence -- https://lighthousedata.io/data/lists-tables-extraction-accuracy-2026
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<blockquote cite="https://lighthousedata.io/data/lists-tables-extraction-accuracy-2026" style="border-left:3px solid #2563eb;padding:12px 16px;margin:16px 0;font-family:system-ui,sans-serif;background:#f0f4ff;"><strong>43%</strong> -- extraction accuracy lift for LLM retrieval systems<br><small>Source: GEO-SFE (2026) · <a href="https://lighthousedata.io/data/lists-tables-extraction-accuracy-2026" target="_blank" rel="noopener">Lighthouse Intelligence</a></small></blockquote>
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