Cornell LII and FindLaw own foundational law. r/legaladvice owns "should I sue." Citation share has become a professional-conduct question.
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01Wikipedia wikipedia.org
Baseline for case law, doctrines, legal history.
T2Encyclopedic -
02Cornell Legal Information Institute law.cornell.edu
Free statutory and case-law reference — the default citation.
T1Academic -
03FindLaw findlaw.com
Consumer-legal authority — practice areas, lawyer marketing.
T3Publisher -
04Justia justia.com
Legal directory and free case-law access.
T3Publisher -
05Nolo nolo.com
Plain-English legal explainers for consumers.
T3Publisher -
06ABA americanbar.org
Bar association — practice standards, ethics, profession news.
T3Trade Press -
07Court & statute .gov sites various .gov
Federal and state court opinions, statutes, regulations.
T1Government -
08State bar associations various .gov
Licensing, ethics, discipline — surfaces on lawyer-selection prompts.
T1Government -
09Reddit reddit.com/r/legaladvice
Owns "should I sue," "can they do this" — uncredentialed.
T4Platform -
10Avvo avvo.com
Lawyer directory with Q&A authority.
T3Publisher
Jurisdiction-specific procedure · recent rulings · attorney selection. Foundational law is locked. Application is wide open. Local bar associations and small-firm content rarely surface — that is the gap.
AI legal hallucinations have triggered active bar sanctions. The source layer is a liability conversation. "Which sources is the engine using" is malpractice context.
- Which sources do AI engines cite most for legal questions?
- Cornell LII, FindLaw, Justia, Nolo, the ABA, court and statute .gov sites, and Wikipedia. Reddit's r/legaladvice and Avvo round out the consumer-facing sources.
- Why is Cornell LII the most-cited legal source in AI answers?
- Free, structured, schema-tagged access to federal and state statutes and case law. The engines retrieve it as the default authority for any prompt requiring a statute or precedent.
- Are AI engines citing r/legaladvice as authoritative?
- Yes — on "should I sue," "can they do this," and small-claims prompts. The engines weight high-engagement community discussion as practical-experience authority.
- What are the malpractice risks of AI citing uncredentialed sources?
- Active bar sanctions have already been issued for AI-generated research that cited unreliable sources. The citation map is now malpractice context, not academic.
- How can law firms increase their AI citation share?
- Influence is indirect. Produce structured, jurisdiction-specific content on prompts with weak institutional coverage. Schema-tagged Q&A and case summaries move share faster than long-form articles.
- Which legal prompts have the most contested source mix?
- Jurisdiction-specific procedure, recent rulings, and attorney-vs-DIY. Cornell and FindLaw cover the foundational layer; everything below is open.
Method
Citation share modeled across four AI engines — ChatGPT, Claude, Perplexity, Google AI Overviews — and a fixed prompt set of 60+ queries spanning informational, transactional, comparison, safety, "best of," and explanatory classes.
Sources tagged on the five-tier Retrieval Hierarchy: T1 Government & Academic · T2 Encyclopedic · T3 Publisher & Trade Press · T4 Community Platforms · T5 Brand-Owned. Estimates are directional and date-stamped.
