You test a prompt in ChatGPT or Perplexity and a different brand keeps appearing. You audit rankings, backlinks, and content and everything looks healthy. Still, the model recommends someone else. The issue is not search performance. It is presence in the sources large language models actually read. If your name and ideas are not present in public forums, technical documentation, open discussions, and widely cited explainers, the model simply never learned you. LLM seeding fixes that gap: it is the deliberate practice of making your expertise visible to the ecosystems that feed these models.
LLM seeding is not the same as traditional search engine optimization. Search engines crawl and index pages; models ingest patterns from conversations, code repositories, community threads, and long-form explainers that circulate and get cited. That difference changes the playbook. Instead of only optimizing for keywords and rank, you must place distinctive, citeable content where models are trained to learn. The three reliable paths are high-authority mentions in technical and niche publications, expert-led explainers that use consistent phrasing, and community citation loops that drive reuse on platforms like Reddit, GitHub, and developer forums. Each path increases the probability a model will associate your brand with a concept or solution.
Practical steps matter. Start with an audit of public mentions for brand names, product names, key people, and signature phrases. Use tools such as Semrush or Ahrefs to map referring domains and traffic sources, then prioritize publishing or contributing to platforms models can crawl and reference. Reframe content to define concepts, offer frameworks that others can quote, and adopt unique, repeatable language. Create resources worth linking to: templates, clear explanations, and durable documentation. Track progress by prompting models with real questions and watching whether your language, frameworks, or brand appear in responses.
This is a long game, not a hack. Models reward repetition and citation, not shortcuts. Aim to guide the model through presence and clarity, not to game it. Over time, well-placed contributions compound; your brand moves from invisible to recognizable in the answers people rely on. That shift matters because visibility in model responses now sits alongside search as a primary channel for discovery.
