Updating your search glossary for the AI era. No academic posturing, no algorithmic panic—just plain-English breakdowns of the terms shaping search.
AI search didn’t just change how users discover content; it completely rewired how search practitioners need to organize it. Somewhere between vector databases, cosine similarity, and “what even is a token budget,” I found myself deep in the weeds of machine learning terms I never planned to learn.
Buzzword Betty is my ongoing series decoding machine learning, RAG retrieval mechanics, and NLP concepts into practical ideas you can actually use.
The Series
Vol. 1: Vector Embeddings & Cosine Similarity
How LLMs turn words into numbers so they can calculate meaning. The core rule: Label with vectors → Compare with cosine → Respond with relevance.
Vol. 2: The New Job Titles of AI Search
Breaking down emerging roles: Relevance Engineers, AI Visibility Strategists, Prompt Strategists, Information Design Leads, and Digital GEOlogists.
Vol. 3: What Is Query Fan-Out?
Demystifying how a single user prompt explodes into dozens of background sub-queries across generative search engines.
Vol. 4: EchoBlocks & Semantic Triplets
Why Subject-Predicate-Object triplets and structured blocks matter for LLM ingestion and unambiguous entity extraction.
Vol. 5: Chunks & Token Budgets
How the smallest units of text make the biggest retrieval impact. Why passages must be self-contained so context travels with the snippet.
Vol. 6: Agentic Actions, Agents & MCPs
Moving from static RAG summaries to multi-agent execution and the emerging Model Context Protocol (MCP).
Buzzword Betty’s (Super Duper) Quick Guide to: AI Performance Report in Bing Webmaster Tools
AI Performance is your backstage pass to how often Bing’s AI uses your content in generated answers across Copilot & AI summaries.