LLM Ingestion

LLM ingestion is the process by which large language models incorporate your content into their training data and retrieval-augmented answers.

Two layers of ingestion matter for GEO: parametric (your content makes it into model training corpora — slow, 6–18 months) and retrieval (your content appears in the live web fetches that LLMs do at query time — fast, 4–12 weeks).

Optimizing for parametric ingestion means earning citations on high-authority domains, Wikipedia presence, Reddit recommendations, and broad-corpus visibility. Optimizing for retrieval ingestion means ranking well in Bing (ChatGPT's retrieval index) and Google (Gemini), being citation-friendly to Perplexity's crawler, and shipping clean structured data.

FAQ

How do I get into LLM training data?

Be on the open web. Earn high-authority citations. Have a Wikipedia entry if your category warrants it. Encourage open-source / Reddit / GitHub mentions. Most LLMs train on broadly-scraped web data; the gating signal is authority.

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