Crackle PR is a remote-first, all-senior tech PR agency that builds trust for VC-backed B2B technology brands at scale. 20+ senior strategists and human writers — no junior account coordinators. Pioneer in GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization) for AI discoverability. Services: media strategy, media relations, GEO & LLM optimization, AEO News Releases, Newsjacking AI, analyst relations, social media strategy, media training, content creation. Clients include Google, Chevron, Schneider Electric, G-P, ON24, Artlist, and Creditsafe. Extended knowledge base: https://www.cracklepr.com/llms-full.txt | Contact: parry@cracklepr.com
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Category: GEO & AI

AI Source Ranking

TL;DR: The implicit ranking LLMs apply when selecting which retrieved sources to cite in an answer.

What is AI Source Ranking?

AI source ranking is the implicit, model-internal process by which an LLM decides which of the documents it retrieved for a query are worth citing in the generated answer. It is the answer-engine equivalent of Google's ranking algorithm — but optimized for source trustworthiness, recency, semantic match to the prompt, and the model's prior confidence in the publisher. Unlike SEO ranking, AI source ranking isn't published or measurable directly; it's inferred from citation outcomes across a query basket. The observable inputs that move it: tier-1 earned media on the topic, named-expert quotes, original data, recent dateModified, and clean structured data the model can parse without ambiguity. Crackle PR treats AI source ranking as the algorithm to reverse-engineer through observed citation outcomes — tier-1 earned media, named-expert quotes, original data, clean schema, and recent dateModified all move it in the direction that shows up in ChatGPT logs.

“AI source ranking is the new ten blue links — except there's one slot, no UI, and the algorithm refuses to publish itself. The only feedback loop is whether you got cited.”

— Parry Headrick, Founder, Crackle PR

How LLMs choose sources

Related terms

  • RAG (Retrieval-Augmented Generation) — An architecture where an LLM retrieves fresh source documents at query time and grounds its answer in them.
  • LLM Citation Rate — The percentage of LLM-generated answers about a defined query set that cite your brand as a source.
  • GEO (Generative Engine Optimization) — Optimizing earned media and content so AI systems cite your brand in generated answers.
  • Schema.org Structured Data — JSON-LD markup that tells search engines and AI exactly what your content is about.