This podcast analyzes a research paper on Generative Engine Optimization (GEO), a new field focused on improving website visibility in generative AI search engines like Google's SGE and Bing Chat. The paper explores how these engines select and cite content, proposing several optimization techniques including improving content authority, adding statistics and citations, and enhancing readability. The analysis highlights the paper's findings, including the success of techniques like "Cite Sources" and "Quotation Addition," while also pointing out some of the paper's limitations and suggesting the need for both on-site and off-site optimization strategies. The post concludes by emphasizing the importance of providing authoritative information across multiple websites to achieve high visibility in generative search results.

This podcast was created using AI and is based on the article at [ Ссылка ]

Transcript

• ​• ​Welcome ​to ​the ​deep ​dive. ​We ​got ​this ​article ​you ​guys ​sent ​about ​generative ​Engine ​optimization. ​GEO ​for ​short. ​Kind ​of ​sounds ​like, ​uh, ​SEO, ​but ​way ​more ​like ​complex.

Speaker B
It ​does? ​Yeah. ​It's ​like ​SEO's ​• ​brainy, ​um, ​• ​• ​cousin, ​maybe. ​We'll ​be ​talking ​about ​how ​people ​creating ​content ​online ​can ​adjust ​their ​strategies ​for ​AI ​search. ​Things ​like ​ChatGPT. ​Oh, ​yeah, ​Google's ​search, ​generative ​experience.

Speaker A
Ge. ​•

Speaker B
Right, ​exactly.

Speaker A
I ​saw ​that ​the ​article ​mentioned ​a ​research ​paper ​from ​some, ​like, ​big ​name ​schools.

Speaker B
Yeah. ​Princeton, ​Princeton, ​Georgia ​Tech.

Speaker A
Yeah, ​they, ​um, ​it ​sounds ​like ​they ​really ​experimented ​with ​different ​techniques ​to ​see ​what ​AI ​search ​engines ​pick ​up ​on.

Speaker B
Yeah, ​they, ​um, ​• ​• ​really ​went ​deep ​and ​we're ​going ​to ​unpack ​some ​of ​the ​more ​interesting ​findings. ​Like ​how ​they ​define ​geo.

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