From SEO to GEO: Your Guide to Getting Cited by AI

RAG Evaluation: The Four Dimensions AI Uses to Judge If Your Content Is Worth Citing

RAG systems commonly evaluate content quality across four dimensions: context relevance, faithfulness, answer relevance, and answer correctness. Conte…
Hybrid Retrieval (BM25 + Vector): Why GEO Content Needs Both Keywords and Semantic Coverage

Hybrid retrieval is the approach most RAG systems use: running traditional BM25 keyword matching and vector semantic retrieval simultaneously, then me…
Grounding and Citation: How AI Decides Who to Cite

Grounding is the process of anchoring AI-generated content to specific information sources. Citation is the visible act of AI attributing content to t…
Why Keyword Stuffing Is Completely Dead in AI Search — Explained Through Vector Retrieval

Keyword stuffing isn't just ineffective in vector retrieval — it's actively harmful: repeating the same word only over-concentrates your content vecto…
Vector Retrieval: AI Doesn’t Match Keywords — It Measures Semantic Distance

Vector retrieval is how RAG systems match information by calculating the semantic distance between query vectors and content chunk vectors. It doesn't…

GEO Basics