The modern AI & LLM glossary.

A large language model is a neural network trained on huge amounts of text to predict the next token in a sequence. GPT-4, Claude, and Gemini are all LLMs.

The transformer is the neural network architecture behind virtually every modern large language model. It uses self-attention to model relationships between all positions in a sequence in parallel.

RAG retrieves relevant documents from a corpus at query time and includes them in the prompt, letting an LLM answer with up-to-date, source-cited, private information without retraining.

RLHF fine-tunes an LLM to maximize a reward model that was itself trained on human preference judgments between candidate responses.

The context window is the maximum number of tokens an LLM can consider in a single call — prompt plus generated output combined.

Mixture of Experts is a transformer variant where each layer has many parallel "expert" feed-forward networks, but only a few are activated per token. Total parameters grow without growing per-token compute.

An AI agent is an LLM-driven system that decides which actions to take, executes them via tools, observes the results, and iterates until a goal is met.

MCP is an open standard for connecting LLMs to external tools and data sources. It defines a JSON-RPC protocol so any client (Claude Desktop, Cursor, IDE plugins) can talk to any MCP server.