Answer Engine Optimization is the practice of engineering content so that LLMs can retrieve, understand, and cite it. Here's how the key frameworks work.
AI search engines expand every query into 5-50 synthetic sub-queries (query fan out). Semantic gap mapping identifies which of those sub-queries your content covers and which ones you're missing entirely.
Think of each sub-query you rank for as a raffle ticket. The more tickets you hold, the more likely you are to get cited. This framework helps you find the tickets you're missing.
LLMs break your pages into passages and index them individually. A paragraph covering two topics scores lower on cosine similarity for both. Splitting that paragraph in half can improve relevance by 10-15%.
Your meta description is an advertisement to the LLM. Your URL slug is a relevance signal. Your data points are what get extracted. Every element plays a role in whether AI search cites you or skips you.
Semantic URLs get 11.4% more citations. Meta descriptions determine if the LLM even fetches your page.
Six and seven word searches have grown for four consecutive quarters while short queries sit flat. People are typing sentences into search boxes, and that changes what content has to answer.
Read More arrow_forwardA saliency map predicts where the eye lands on a screen before intent kicks in. What saliency actually measures, the four features that drive it, how it differs from a heatmap and from eye tracking, and where it stops being useful.
Read More arrow_forwardClicks to Google-owned properties rose from 11.7% to 20.4% in a year while organic click-through fell to 40.7%. Zero-click stayed flat, which means the clicks did not vanish. They were redirected.
Read More arrow_forward