Generative Engine Optimization (GEO) or “LLM seeding” is the practice of publishing content in the formats and places LLMs are most likely to scrape, summarize, and cite. LLM seeding includes:
- What to publish so LLMs actually cite you
- Where to seed your content for maximum pickup
- And how to track whether your brand is showing up
Resources
LLM vs. SE
According to a Semrush study AI search traffic will surpass traditional search by the end of 2027.
| Aspect | LLMs (ChatGPT, Claude, etc.) | Search Engines (Google, Bing, etc.) |
|---|---|---|
| How they answer questions | Generate direct answers by synthesizing knowledge from training data | Return ranked links to web pages based on keywords and relevance |
| Source of information | Trained on snapshots of web content (and sometimes licensed datasets) | Crawl the live web continuously |
| Citations | May include sources (e.g. ChatGPT with web access), but often without deep linking | Provide direct links to source websites |
| Up-to-dateness | May be outdated (depends on last training or retrieval capability) | Near real-time indexing (updated frequently) |
| Interactivity | Conversational, can refine or continue context-aware discussions | One-shot queries with static results |
| Bias toward content types | Prefers well-written, generalizable, structured content seen during training | Ranks content based on SEO, backlinks, page authority |
| Visibility requirements | Needs content to be public, high-quality, and referenced before training | Indexes any crawlable page, regardless of popularity |
| Personalization | Personalized in some tools (e.g. ChatGPT memory), not by search history yet | Highly personalized by user history, location, preferences |
| Interaction granularity | Can summarize, rephrase, explain, and code – beyond just pointing to data | Mostly lists URLs/snippets with minimal interpretation |
| Use cases | Learning, summarizing, code writing, brainstorming, complex reasoning | Finding exact websites, news, shopping, fast fact-checking |
| Content lifespan in model | May persist for months or years in training snapshots | Can update rankings/content visibility within hours to days |
| User intent targeting | Understands semantic intent, not just keywords | Primarily matches keywords and metadata |
LLM Preferences & Tools
- LLMS finds results based on meaning rather than exact keywords
- LLMs give weight to content mentioned or linked by well-established sources.
- LLMs are more likely to find and understand content when it’s structured.
- LLMs learn from publicly accessible question-answer pairs and guides.
- LLMs can also pull from video transcripts and descriptions.
Fun fact: According to a Semrush study, almost 90% of ChatGPT citations come from search engine positions 21+.
Tools that can be used to train or impact LLMs:
| Tool | What It Is | Used By | What It Does | Does It Affect LLM Training? |
|---|---|---|---|---|
| LLMS.txt | A proposed standard file (like robots.txt) | AI crawlers (e.g. OpenAI, Anthropic, etc.) | Tells AI bots which pages can be used for training or retrieval | ✅ Yes, if respected by the crawler |
robots.txt | Standard web control file | Search engines & AI bots | Allows/disallows crawling of pages | ✅ Affects both SEO and LLM access |
| Knowledge Graph (KG) (similar to schema.org in MkDocs) | Structured metadata embedded in web pages | Google, Bing, sometimes LLMs | Helps machines understand context (e.g. that a page is about a software tool) | ⚠️ Not always used directly in LLM training, but useful for context and SEO |
| Open Graph / Twitter Cards | Social metadata for sharing previews | Facebook, Twitter/X, some AI scrapers | Helps format link previews | ❌ No direct impact on LLM training |
| Sitemaps | List of all URLs on your site | Crawlers, search engines | Helps bots find your pages | ✅ Indirectly useful to LLMs if they crawl |
| API docs with OpenAPI / Swagger | Structured API definitions | Dev tools, LLM code generators | Helps with tool integrations & understanding your API | ✅ Can be used in some LLMs (e.g. tool use, code generation) |
TODOs
Optimize for citations instead of clicks:
- Make sure your website and documentation are crawlable (check robots.txt and meta tags).
- Include AI-friendly content like:
- detailed technical guides and tutorials
- FAQs (refer to common pre-sales tickets)
- comparison posts (compare products to competitors, for our own products use “best of” lists like “best for Alteryx users”, “best all-in-one solution”, etc.)
- real use cases
- come up with unique takes on something in your industry.
- Use AI-friendly formatting like:
- bullet points
- clear headings
- short paragraphs (chunks)
- short, declarative lines
- summary boxes
- Use blog posts or pages with headlines like:
- “How to use [Your Product] for [X Problem]”
- “[Your Product] vs. [Competitor]”
- “10 Tips for Getting the Most from [Your Product]”
- Get referenced by other reputable sources, e.g.:
- ask partners to link to our website
- ask users or tech bloggers to review our tool or write tutorials referencing it.
- Submit guest posts to relevant publications and social media with backlinks to the docs or site
- appear on websites like Product Hunt, AlternativeTo
- Use schema.org metadata (e.g.,
SoftwareApplicationfor your product,Articlefor blog posts). - In YouTube videos, use sections and descriptions that include:
- who you are and why you’re qualified to cover the topic
- state what the video covers early on
- link to related posts or supporting content
- Make sure visuals like images include:
- alt text
- full-sentence captions that explain what’s pictured and why it matters
- reference images in the text (“As illustrated in the following screenshot…”)
- use descriptive file names (“xtract-universal-designer-main-menu.jpg”)
- Publish content in places LLMs look for information like:
- Medium
- LinkedIn articles
- industry sources (magazines)
- roundups (“best of,” “top tools,” and “expert tips” formats)
- user-generated content hubs like Quora, gitHub discussions and niche / specialized forums (LLMs cite Reddit more than any other source, according to Semrush)
Example
Content:
- Item name
- Quick summary
- Key features or standout capabilities
- Pros and cons
- Pricing
How to Test Results
Run manual prompts across different tools like ChatGPT, Claude, Perplexity, and Gemini. Use a private or incognito browser to avoid skewed results from past queries or personalization. Search the way your audience would with clear search intent.
A sign that your LLM presence increases is an increase in impressions and direct traffic while clicks decrease.