LLM Seeding

LLM seeding is the practice of placing consistent, quotable information about your brand on sites that AI models are likely to retrieve or learn from, instead of relying only on your own website. Typical targets are review platforms, community forums, industry publications, comparison articles, Q&A sites and video descriptions.

The idea rests on how answer engines work. They often draw on third-party mentions when judging which brands to name, and they cite sources outside the brand's own domain. If a category's cited sources do not mention you, seeding aims to put accurate facts there. It overlaps with digital PR and with training data optimization, which targets what models absorb during training rather than what they retrieve at answer time.

Communities are a common channel, though their value changes by engine. Reddit citations in ChatGPT fell sharply in August 2026 in our data, which we cover in why ChatGPT stopped citing Reddit, while other engines still cite threads. UGC citations more broadly remain worth checking per engine before investing. For a practical walkthrough, see how to get your brand mentioned by AI.

Seeding has limits. Most published advice on it comes from practitioners, and we are not aware of a peer-reviewed study showing that a given placement tactic reliably changes AI answers. Models retrieve different sources from run to run, so results are uneven. Low-quality or undisclosed promotional posts also tend to be removed by moderators and can create negative sentiment instead.

A sound approach is to publish accurate, specific, verifiable information, disclose your affiliation, and measure outcomes. Track whether the seeded sources start appearing as citations and whether your mention rate changes on the prompts you care about. Reddit marketing tooling can help with the community part of this work.