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Plain-language AI glossary

Term 02FundamentalsMeaning / context / connections

Fundamentals / Definition

Transformer

The neural network architecture behind all modern LLMs, using self-attention mechanisms to process sequences in parallel.

Browse all termsUnderstand the idea
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01

MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

03

UsesWhere the concept becomes useful.

01 / Plain-language context

How Transformer works.

Introduced in the 2017 paper "Attention Is All You Need," transformers replaced older recurrent architectures (RNNs, LSTMs) by processing entire sequences simultaneously using attention mechanisms. This parallelism made training on massive datasets feasible. Every major AI model today (GPT, Claude, Gemini, Llama) is based on the transformer architecture.

02 / Practical uses

Where it helps.

  1. 01Language understanding
  2. 02Text generation
  3. 03Image recognition (Vision Transformers)
  4. 04Audio processing
03 / Keep exploring
Large Language Model (LLM)A neural network trained on massive text datasets that can generate, understand, and reason about human language.Attention MechanismA neural network component that allows models to dynamically focus on the most relevant parts of the input when generating each token of output.TokenizationThe process of breaking text into smaller units (tokens) that an AI model can process, typically subwords or word pieces.