# AI and search glossary > Plain-English definitions of 30 terms buyers meet in AI and search projects, from RAG and embeddings to GEO, llms.txt and n8n. - Page: https://methodcrew.com/glossary - Last updated: 2026-10-28 - Publisher: [The Method Crew](https://methodcrew.com/) - **[Agentic AI](https://methodcrew.com/glossary/agentic-ai)** (agentic systems): Agentic AI is the umbrella term for systems where language models plan steps and take actions through tools, with less step-by-step direction from people. - **[AI agent](https://methodcrew.com/glossary/ai-agent)** (LLM agent, autonomous agent): An AI agent is a language model that chooses and calls tools in a loop, reading each result, until it finishes a task or hands off to a person. - **[AI copilot](https://methodcrew.com/glossary/ai-copilot)** (embedded AI assistant): An AI copilot is an assistant built into software your team already uses that drafts and suggests work, while a person makes every final decision. - **[AI guardrails](https://methodcrew.com/glossary/ai-guardrails)** (LLM guardrails): AI guardrails are checks placed around a language model that block, rewrite or escalate inputs and outputs that break your rules on safety, accuracy, privacy or scope. - **[AI hallucination](https://methodcrew.com/glossary/ai-hallucination)** (confabulation): An AI hallucination is output from a language model that is fluent and confident but false, such as an invented citation, policy, figure or product detail. - **[AI visibility (share of voice in AI answers)](https://methodcrew.com/glossary/ai-visibility)** (AI search visibility, LLM visibility): AI visibility measures how often AI assistants mention or cite your business when answering the questions your buyers ask, compared with competitors over the same set of prompts. - **[Answer engine optimization (AEO)](https://methodcrew.com/glossary/answer-engine-optimization)**: Answer engine optimization structures pages so a search engine or AI assistant can lift a direct answer from them, as a featured snippet, voice answer or AI citation. - **[Chunking](https://methodcrew.com/glossary/chunking)** (text splitting): Chunking is splitting documents into smaller passages before they are embedded and indexed, so a retrieval system can find and return the exact part that answers a question. - **[Context window](https://methodcrew.com/glossary/context-window)** (context length): A context window is the maximum amount of text, measured in tokens, that a language model can read and write in one request, counting instructions, documents and its reply. - **[Embeddings](https://methodcrew.com/glossary/embeddings)** (vector embeddings): Embeddings are lists of numbers that capture the meaning of text or images, so software can measure how closely related one item is to another. - **[Entity SEO](https://methodcrew.com/glossary/entity-seo)** (entity optimization): Entity SEO is making sure search engines and AI systems recognize your business as one distinct thing, with consistent facts about what it is, does and connects to. - **[Fine-tuning](https://methodcrew.com/glossary/fine-tuning)**: Fine-tuning is further training of an existing language model on your own examples, so it learns a specific format or task without long instructions every time. - **[Function calling (tool use)](https://methodcrew.com/glossary/function-calling)** (tool calling): Function calling lets a language model ask your software to run a named function, such as a CRM lookup, by returning the function name and arguments as JSON. - **[Generative engine optimization (GEO)](https://methodcrew.com/glossary/generative-engine-optimization)** (LLM optimization (LLMO)): Generative engine optimization is the work of making your pages likely to be retrieved, quoted and cited in AI-generated answers from ChatGPT, Perplexity, Gemini and Google AI Overviews. - **[Google AI Overviews and AI Mode](https://methodcrew.com/glossary/ai-overviews)** (Search Generative Experience (SGE)): AI Overviews are AI-generated summaries shown at the top of some Google results with links to sources; AI Mode is a conversational search view built on the same systems. - **[Human-in-the-loop (HITL)](https://methodcrew.com/glossary/human-in-the-loop)** (human review): Human-in-the-loop is a workflow design where a person reviews, corrects or approves an AI system's output at set points before it reaches a customer or a system of record. - **[Hybrid search](https://methodcrew.com/glossary/hybrid-search)** (hybrid retrieval): Hybrid search runs keyword search and semantic search on the same query and merges the results, so it catches exact terms and paraphrased meaning together. - **[Intelligent document processing (IDP)](https://methodcrew.com/glossary/intelligent-document-processing)** (document AI): Intelligent document processing uses OCR and AI models to read documents such as invoices, contracts and forms, extract the fields you need, check them and post them into your systems. - **[Large language model (LLM)](https://methodcrew.com/glossary/large-language-model)**: A large language model is a neural network trained on huge volumes of text to predict the next token, which lets it write, summarize, classify and answer questions. - **[LLM evaluation (evals)](https://methodcrew.com/glossary/llm-evaluation)** (LLM testing): LLM evaluation scores a language model system's outputs against a fixed set of real test cases, so you can tell whether a change made it better or worse. - **[LLMOps (AI observability)](https://methodcrew.com/glossary/llmops)** (LLM observability): LLMOps is the operating discipline for language model systems in production: tracing every call, tracking cost and latency, monitoring quality, and managing prompt and model changes. - **[llms.txt](https://methodcrew.com/glossary/llms-txt)**: llms.txt is a proposed Markdown file at a site's root that gives AI tools a short site summary and a hand-picked list of links to its key pages. - **[Model Context Protocol (MCP)](https://methodcrew.com/glossary/model-context-protocol)**: The Model Context Protocol is an open standard for connecting AI applications to outside tools and data, so a single integration works across compatible assistants and agents. - **[n8n](https://methodcrew.com/glossary/n8n)**: n8n is a workflow automation platform, self-hosted or cloud, that connects apps and APIs in visual workflows and supports custom code and AI agent steps. - **[Prompt engineering](https://methodcrew.com/glossary/prompt-engineering)** (prompt design): Prompt engineering is writing and testing the instructions, examples and context given to a language model so it produces the output a task needs, consistently. - **[Prompt injection](https://methodcrew.com/glossary/prompt-injection)**: Prompt injection is an attack where text given to a language model, typed by a user or hidden in a document, overrides the instructions the system was built with. - **[Retrieval-augmented generation (RAG)](https://methodcrew.com/glossary/retrieval-augmented-generation)** (grounded generation): Retrieval-augmented generation is a method where a system first searches your own documents for relevant passages, then gives them to a language model to answer from. - **[Schema markup (structured data)](https://methodcrew.com/glossary/schema-markup)** (schema.org markup): Schema markup is structured data, usually JSON-LD, that labels the facts on a page, such as organization, service, price or author, using the shared schema.org vocabulary. - **[Semantic search](https://methodcrew.com/glossary/semantic-search)** (vector search, neural search): Semantic search finds results by meaning instead of exact words, so a query can match relevant content even when they share no vocabulary. - **[Vector database](https://methodcrew.com/glossary/vector-database)** (vector store): A vector database stores embeddings, the numeric fingerprints of text or images, and quickly finds the stored items whose meaning is closest to a query.