What is Attention Mechanism?
A technique that lets AI models focus on the most relevant parts of the input when generating output.
Why It Matters
Attention is what allows language models to understand context and produce coherent, relevant responses.
Real-World Example
When translating a sentence, attention helps the model focus on the right source words for each translated word.
“Understanding terms like Attention Mechanism matters because it helps you have better conversations with developers and make smarter decisions about your software. You do not need to be technical. You just need to know enough to ask the right questions.”
Related Terms
Transformer
A type of AI architecture that processes text by paying attention to relationships between all words at once, rather than reading sequentially.
Self-attention
A mechanism where each word in a text considers its relationship to every other word in the same text.
Cross-attention
A mechanism where one sequence of data attends to another, enabling models to connect different inputs.
Attention Heads
Parallel attention mechanisms within a transformer that each focus on different types of relationships in the input.
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Related Terms
Transformer
A type of AI architecture that processes text by paying attention to relationships between all words at once, rather...
Attention Heads
Parallel attention mechanisms within a transformer that each focus on different types of relationships in the input.
Self-attention
A mechanism where each word in a text considers its relationship to every other word in the same text.
Cross-attention
A mechanism where one sequence of data attends to another, enabling models to connect different inputs.
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human language.
Tokenisation
The process of breaking text into smaller pieces called tokens that an AI model can process.