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Plain-language AI glossary

Term 74ModelsMeaning / context / connections

Models / Definition

Transformer Architecture (Detailed)

The complete technical architecture of the Transformer, including multi-head self-attention, positional encoding, feed-forward layers, and the encoder-decoder structure.

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01 / Plain-language context

How Transformer Architecture (Detailed) works.

The Transformer architecture, introduced in 2017, is the backbone of all modern AI. Understanding its components helps you reason about model capabilities and limitations.

Self-Attention: The core innovation. For each token, the model computes how much "attention" to pay to every other token in the sequence. This is done through Query, Key, and Value matrices. The attention score between two tokens is the dot product of their Query and Key vectors, normalized and applied to Value vectors. This allows the model to relate distant tokens ("The cat sat on the mat because it was tired": attention connects "it" to "cat").

Multi-Head Attention: Instead of one attention calculation, the model runs multiple in parallel (e.g., 96 heads). Each head can learn different relationship types: one might track grammar, another semantics, another long-range dependencies.

Positional Encoding: Since attention processes all tokens simultaneously (no inherent order), position information is added via sinusoidal functions or learned embeddings. Modern models use RoPE (Rotary Position Embeddings) for better handling of varying sequence lengths.

Feed-Forward Layers: After attention, each token passes through a feed-forward network that transforms its representation. This is where much of the model's "knowledge" is stored.

Modern Variants: Decoder-only (GPT, Claude, Llama: used for generation), Encoder-only (BERT: used for classification/embeddings), Encoder-Decoder (T5, original Transformer: used for translation).

02 / Practical uses

Where it helps.

  1. 01Understanding LLM capabilities and limitations
  2. 02Model architecture selection
  3. 03AI research and development
  4. 04Optimizing inference performance
  5. 05Building custom model architectures
03 / Keep exploring
TransformerThe neural network architecture behind all modern LLMs, using self-attention mechanisms to process sequences in parallel.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.Large Language Model (LLM)A neural network trained on massive text datasets that can generate, understand, and reason about human language.Neural NetworkA computational system inspired by the brain, composed of layers of interconnected nodes (neurons) that learn patterns from data through training.Foundation ModelA large, general-purpose AI model trained on broad data that serves as a base for many downstream tasks through fine-tuning, prompting, or adaptation.