The NSY AI & web glossary

By Cédric Barme · Founder of NSY

The terms that come up in every web + AI project, explained without jargon by someone who implements them — not just talks about them. Each definition fits in a few sentences; for concrete cases, see the FAQ or the AI website offering.

Artificial intelligence

LLM (Large Language Model)

A large language model: an AI trained on vast text corpora, able to understand and produce natural language. Claude (Anthropic), GPT (OpenAI) and Mistral are the most widely used in business.

Token

The unit a LLM splits text into — roughly ¾ of an English word. API billing and context-window size are measured in tokens: mastering this unit means mastering your costs.

Context window

The amount of text a LLM can "keep in mind" within one exchange. It determines the size of documents that can be analysed in one pass — a major selection criterion between models.

Prompt

The instruction given to the model. Prompt quality (context, constraints, examples, expected format) often does more for the result than the choice of model itself.

Hallucination

A wrong answer delivered with confidence. Unavoidable in free generation, it is controlled through RAG (answers grounded in your documents), guardrails and human review on high-stakes uses.

Embeddings

Representing a text as a numeric vector that captures its meaning. Two semantically close texts have close vectors — the building block of semantic search.

Semantic search

Searching by meaning rather than exact keywords: "parental leave" also retrieves documents about "birth" or "long-term absence". Built on embeddings.

RAG (Retrieval-Augmented Generation)

The architecture pattern that makes an assistant reliable: relevant documents are retrieved first (semantic search), then the LLM answers from them, with sources — instead of inventing.

AI agent

A LLM that does more than answer: it acts through tools (querying a database, filling a form, calling an API), across multiple steps, under guardrails. See Claude integration.

Fine-tuning

Partially retraining a model on your data to specialise its behaviour. Often unnecessary: a good prompt plus RAG covers most needs, at a fraction of the cost.

MCP (Model Context Protocol)

An open standard (initiated by Anthropic) describing how a LLM connects to external tools and data sources — a universal port between models and enterprise systems.

No-LLM chatbot

A rule-based intent engine: free to run, instant, no API key and no data leaving the site. Right when the question domain is bounded — this site's own chatbot works that way.

Web & search

SEO (Search Engine Optimization)

Optimising for classic search engines: content, technical health (speed, mobile), internal linking and authority. The foundation — without it, nothing else works.

Semantic SEO

Structuring meaning beyond keywords: named entities, structured data, consistency of facts across pages. It is what lets engines — and AIs — understand who you are.

GEO (Generative Engine Optimization)

Getting cited by generative engines (ChatGPT, Claude, Gemini, Perplexity). The levers: attributable, dated content, cross-checkable entities (Wikidata, official registries), llms.txt files, conversational FAQs.

LLMO (LLM Optimization)

GEO's cousin: optimising how LLMs understand your site during training or browsing — allowing their crawlers, providing structured context, avoiding content reachable only through JavaScript.

llms.txt

A file at a site's root (llmstxt.org convention) summarising for AIs who you are, what you do and where to find the essentials. NSY's: nsy.fr/llms.txt.

JSON-LD / structured data

Invisible markup (schema.org) describing your pages in machine language: organisation, people, services, prices, FAQ. The raw material of Google's Knowledge Graph and of assistant answers.

hreflang

Tags declaring the language versions of a page (fr/en…) so every visitor — and every engine — gets the right language, without being counted as duplicate content.

Core Web Vitals

Google's metrics measuring real experience: display speed (LCP), responsiveness (INP), visual stability (CLS). They weigh on rankings — and above all on your visitors' patience.

AI crawler

An AI player's exploration robot (GPTBot, ClaudeBot, PerplexityBot…). Allowing them in robots.txt is the entry condition for existing in assistant answers — blocking them writes you out of their corpora.

Edge hosting

Serving your site from servers distributed close to visitors (Cloudflare, Vercel): minimal latency everywhere, higher resilience — relevant as soon as your audience spans more than one region.