LLMs are trained on human-generated text that includes enormous quantities of statistics. When an AI system retrieves supporting evidence for a factual claim, it preferentially selects sources that provide specific, citable numbers over those that describe trends in vague terms. Publishing original, well-sourced statistics is one of the fastest routes to becoming an LLM-cited authority.
Types of Statistics LLMs Cite Most
Specific percentages tied to named studies, year-stamped benchmark figures, and comparative statistics ("LLMs hallucinate 4–7 times more without RAG") are cited far more often than rounded estimates ("most AI systems make mistakes"). The specificity signals that the figure comes from a real source, which increases LLM retrieval confidence.
How to Generate Citable Statistics
You do not need a research team. Survey your own user base, analyse your platform's aggregated anonymised data, or synthesise publicly available datasets into a new benchmark. Publish the methodology alongside the figure. Statistics with transparent methodology are treated as primary sources by LLMs — the highest trust tier in retrieval-augmented systems.
Keeping Statistics Fresh
Update key statistics annually and mark the update date prominently. A 2024 statistic updated to 2026 figures signals active maintenance. LLMs trained on 2026 data will cite the 2026 version; those trained on 2024 data will cite the 2024 version. By maintaining both versions on the same canonical URL, you maximise citation probability across LLM generations.