Category
AI & LLMs
The model-layer vocabulary you meet across tool pages, comparisons, and stacks - what the words mean before you pick a tool.
- Models
- Prompting
- Retrieval
- Agents
11 terms
AI agent
An AI agent is a system that uses a language model to decide and take actions - calling tools, running steps in a loop - toward a goal, rather than just answering once.
Context window
The context window is the maximum amount of text (measured in tokens) a language model can consider at once, including your prompt and its own reply.
Embedding
An embedding is a list of numbers (a vector) that represents the meaning of a piece of text, image, or code so that similar things sit close together.
Fine-tuning
Fine-tuning further trains a pretrained model on your own examples so it adapts to a specific task, tone, or format.
Hallucination
A hallucination is when a language model produces confident but false or unsupported output - a made-up fact, citation, or API.
Large language model (LLM)
A large language model is a neural network trained on huge amounts of text to predict the next token, which lets it generate and reason over language.
Model Context Protocol (MCP)
MCP is an open protocol that lets AI agents discover and call external tools and data sources over a standard interface, instead of every app inventing its own.
Retrieval-augmented generation (RAG)
RAG fetches relevant documents at query time and feeds them to a language model as context, so answers are grounded in your data instead of only the model's training.
Semantic search
Semantic search matches on meaning instead of exact keywords, usually by combining embeddings with traditional text ranking.
Token
A token is the small chunk of text (roughly a word-piece) that language models read and generate; usage and limits are counted in tokens, not characters.
Vector search
Vector search finds the items whose embeddings are closest to a query embedding, ranking by meaning rather than exact word matches.