Retrieval-Augmented Generation (RAG) is a technique where an AI model retrieves relevant external documents or data at the moment of answering a query, rather than relying only on what it learned during training. This lets the model produce more current, source-grounded answers instead of depending entirely on parametric memory. RAG in Practice When you ask…
Parametric memory is the knowledge a language model encodes directly into its weights during training, which it draws on to generate answers without consulting any external source at the moment it’s asked a question. Parametric Memory in Practice Ask a model something well-established and older than its training cutoff — “what is the capital of…
Chunking is the process of breaking a page or document into smaller, self-contained sections so a retrieval system can index, extract, and cite one piece of it without needing the surrounding text for context. Chunking in Practice A well-chunked FAQ page, where each question-and-answer pair stands completely on its own, gives a retrieval system a…
Semantic chunking is a specific method of splitting content at natural topic or meaning boundaries — rather than at a fixed character or word count — so each resulting chunk represents one complete idea. Semantic Chunking in Practice Naive chunking might cut a document every 500 characters regardless of what’s happening in the sentence at…
LLM visibility is the broader question of whether a brand, entity, or piece of content is represented at all inside a large language model’s outputs — including its training-data knowledge (parametric memory) and its retrieval indexes — separate from whether it’s specifically cited as a source in any single answer. LLM Visibility in Practice A…
Share of model is the percentage of AI-generated answers or recommendations within a product or service category that mention a specific brand, relative to how often competing brands are mentioned for the same set of prompts — the AI-search counterpart to share of voice. Share of Model in Practice If someone asks ChatGPT “what’s the…
Vector embeddings are numerical representations of text (or other data) placed in a high-dimensional space, positioned so that content with similar meaning sits closer together in that space — regardless of whether the actual words used are the same. Vector Embeddings in Practice The words “affordable” and “budget-friendly” share almost no letters in common, but…
Content atomization is the practice of breaking a single larger piece of content — a report, guide, or pillar post — into smaller, independently valuable pieces, each tailored to a specific platform, format, or audience segment, rather than simply repurposing the same material into a different format. Content Atomization in Practice A comprehensive guide on…
Generative Engine Optimization (GEO) is the practice of structuring content and brand presence so that AI systems — ChatGPT, Perplexity, Gemini, Claude — cite, quote, or reference your source when synthesizing an answer, rather than optimizing for a ranked link a person clicks. GEO in Practice A 2026 study from Ranqo, which analyzed over 100,000…
Answer Engine Optimization (AEO) is the practice of structuring content to be selected as a direct answer by engines that return one answer instead of a list of links — historically featured snippets, People Also Ask boxes, and voice assistant responses, now extending to AI Overviews and chat-based answers as well. AEO in Practice A…
