Definition
The foundational neural network design utilizing self-attention mechanisms to process sequential data in parallel, serving as the core computational engine for LLMs to interpret retrieved documents and generate agentic responses. It prioritizes global context capture through quadratic computational scaling, enabling the complex reasoning required for RAG synthesis.
Distinguish from physical electrical transformers; refers to the mathematical framework for sequence-to-sequence modeling used in LLMs.
"A high-speed sorting facility where every package is scanned simultaneously and cross-referenced with every other package to determine its priority and destination."
- Self-Attention(Component)
- Encoder-Decoder(Component)
- Positional Encoding(Component)
- Context Window(Architectural Constraint)
Conceptual Overview
The foundational neural network design utilizing self-attention mechanisms to process sequential data in parallel, serving as the core computational engine for LLMs to interpret retrieved documents and generate agentic responses. It prioritizes global context capture through quadratic computational scaling, enabling the complex reasoning required for RAG synthesis.
Disambiguation
Distinguish from physical electrical transformers; refers to the mathematical framework for sequence-to-sequence modeling used in LLMs.
Visual Analog
A high-speed sorting facility where every package is scanned simultaneously and cross-referenced with every other package to determine its priority and destination.