How to use vector embeddings in AEO

HubSpot's guide on “How to use vector embeddings in AEO” explains that embedding models convert text into numerical vectors, enabling retrieval systems to match semantically similar passages even when the wording differs. For website owners, this means AI‑driven content recommendation engines can now surface related blog posts, product pages, or support articles with far greater relevance, driving higher engagement and SEO value. However, the increased use of vector search also generates distinctive bot traffic patterns—frequent short queries with high‑dimensional payloads—that can inflate analytics and obscure genuine user behavior. **Actionable tips:** 1. **Update your llms.txt** to explicitly disallow generic crawlers from accessing endpoints that serve raw embedding vectors, reducing unnecessary load and protecting proprietary model outputs. 2. **Implement bot fingerprinting** in your analytics stack to flag the characteristic high‑frequency, low‑session‑duration requests typical of embedding‑based AI bots, allowing you to separate them from human visitors. 3. **Monitor vector query logs** for spikes that may indicate misuse or scraping of your embedding API, and set rate limits or require API keys for external services accessing these endpoints.