Glossary
Attention Mechanism
An attention mechanism lets a neural network weigh which tokens in its available context are most relevant to one another. In transformers, self-attention helps the model represent relationships across a sequence, but it does not itself search the web or verify whether a claim is true.
In plain terms
Attention helps the model decide which parts of the text in front of it matter for the next output.
Why it matters
It explains why clear local context helps, while also showing why attention is not evidence or factual verification.
How to apply it
- Use explicit subjects instead of ambiguous pronouns.
- Keep claims and supporting context together.
- Do not market formatting tricks as ways to control model attention.
Example
In a retrieved paragraph, the model can connect a product name with its stated audience and limitation because those tokens appear together.
Sources
Related reading
Related terms
Back to the full glossary (75 terms).
Ranking is no longer enough
You need to be cited, mentioned, and recommended.
Being cited, mentioned, and recommended are three different outcomes, and most brands only ever achieve the first one. Ranking is no longer enough because AI engines answer buyers directly and name only a short list of vendors as the recommendation — everyone else is cited in passing, if at all. Get a free AI Visibility Report to see exactly where your brand appears today across ChatGPT, Google AI Overviews, Gemini, Perplexity, and Copilot, where competitors are winning the recommendation instead, and what's keeping you from moving up the shortlist.