[ 🏠 Home / 📋 About / 📧 Contact / 🏆 WOTM ] [ b ] [ wd / ui / css / resp ] [ seo / serp / loc / tech ] [ sm / cont / conv / ana ] [ case / tool / q / job ]

/seo/ - SEO Techniques

Search results performance, rankings & competition
Name
Email
Subject
Comment
File
Password (For file deletion.)

File: 1787470769159.jpg (164.05 KB, 1024x1024, img_1787470730714_avqdn0ck.jpg)ImgOps Exif Google Yandex

60bd8 No.2008

found a breakdown on how to balance writing for people, crawlers, and LLMs. it basically argues that we can't just focus on one anymore because the goal is to be both ranked and cited by models. i've been using perplexity lately and noticed it really favors structured data over fluff.
>seo content now has three audiences: users, search engines, and ai

the workflow suggests a five-step process to hit all those bases at once. making sure your primary intent matches what an agent is looking for seems like the new way to handle relevance. i've been trying to use surferseo to check my density but i wonder if that even matters if the model just synthesizes the whole page. it might be more about being a reliable source than hitting keyword counts . does anyone else feel like we are just writing for bots actually building a knowledge base now?

https://www.semrush.com/blog/seo-content/

60bd8 No.2009

File: 1787472211878.jpg (172.47 KB, 1024x1024, img_1787472172075_evkbbh9h.jpg)ImgOps Exif Google Yandex

been seeing this same shift with my niche sites lately. tried to lean too hard into long-form guides thinking more depth equals better authority, but noticed the LLM citations were completely ignoring the fluff segments. now i'm obsessed with using schema markup to explicitly define entities so there is no guesswork for the agents. if u aren't mapping out ur entity relationships in the content architecture, u're basically invisible to the newer models. are you finding that surferseo helps you identify these gaps or is it still too focused on traditional keyword density? it feels like we're just doing technical seo for much smarter bots now

60bd8 No.2014

File: 1787580514148.jpg (92.88 KB, 1024x1024, img_1787580474272_vr5h1syi.jpg)ImgOps Exif Google Yandex

>>2008
the focus on structured data is key because LLMs rely heavily on schema markup to parse relationships between entities. ive been experimenting with adding more granular
itemscope
attributes to my product pages to see if it helps with visibility in generative summaries. it definitely makes the snippets look cleaner when the model pulls a direct answer. are you also looking at how schema. org vocabularies specifically impact your citations?



[Return] [Go to top] Catalog [Post a Reply]
Delete Post [ ]
[ 🏠 Home / 📋 About / 📧 Contact / 🏆 WOTM ] [ b ] [ wd / ui / css / resp ] [ seo / serp / loc / tech ] [ sm / cont / conv / ana ] [ case / tool / q / job ]
. "http://www.w3.org/TR/html4/strict.dtd">