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Glossary

Short explainers for LLMs, agents, training, and related concepts.

Also see: Timeline · Predictions · Articles.

A

  • AI agent

    A system that pursues goals by observing an environment, planning, and taking actions (often via tools) rather than only producing static text.

  • AI winter

    A period when AI research funding and public optimism dropped sharply after early hype exceeded practical results.

D

  • Deep learning

    Neural networks with many successive layers and training methods tuned to stacked representations (often rebranded circa 2006–2012 revival).

F

  • Fine-tuning

    Additional training starting from an existing model: usually smaller data and narrower objective than pretraining.

I

  • Inference

    Using a trained model to produce outputs on new inputs: after training is finished (also called deployment or forward pass in many setups).

L

  • Large language model (LLM)

    A statistical language model trained on large text corpora, typically using a transformer architecture, used for generation and understanding tasks.

M

  • Machine learning (ML)

    Algorithms that generalize patterns from data to make predictions or decisions without hand-written rules for every case.

N

  • Neural network (ANN)

    Parametric models organized in layers of simple units; strengths are learned from data via optimization.

P

  • Prompt

    The textual instruction or context given to an AI model to guide output style, scope, and task.

R

  • Reasoning

    Step-by-step inference behavior used to derive conclusions from context, rules, or intermediate states.

  • Reinforcement learning (RL)

    Learning policies via rewards or scores from interacting with an environment: core to games, robotics, and some RLHF-style LLM tuning.

  • Retrieval-augmented generation (RAG)

    Combining a retriever over documents or tools with a generator LLM so answers can cite fresher or private context.

S

T

  • Token

    The smallest text unit a language model processes (often a word piece, not a full word).

  • Training (model training)

    The phase where model parameters are optimized on data or feedback before deployment: distinct from inference at run time.

  • Transformer (architecture)

    Neural sequence model built mainly on attention: parallelizable and foundational for LLMs since "Attention Is All You Need" (2017).

  • Turing test (imitation game)

    Alan Turing’s proposed behavioral criterion: can dialogue from a machine be distinguished from a human’s?