How it works, roughly
Text is converted into vectors, numeric representations that place similar meanings near each other. A query becomes a vector too, and retrieval finds the content sitting closest to it, whether or not the words match.
This is the same machinery behind generative answers. A model retrieves candidate passages semantically before it writes anything, which is why passage quality matters more than keyword placement.
| Keyword matching | Semantic matching | |
|---|---|---|
| Matches on | Strings in the text | Meaning of the passage |
| Rewards | Exact phrase placement | Topic coverage and clarity |
| Penalises | Little | Repetition and thin content |
| Implication | Write for a phrase | Write for a question |
What it changed for writing
Exact-match keyword density stopped being useful and started being a liability. Repeating a phrase to hit a target now reads as thin to systems that were built to recognise meaning, and Princeton's 2024 research on generative engines found keyword stuffing performed worse than doing nothing.
- Cover the topic properly rather than chasing one phrase
- Use the vocabulary your readers use, including synonyms and adjacent terms
- Write self-contained sections, since retrieval works at passage level
- Define your terms, because clear definitions are easy to match and easy to quote
Why it matters for B2B marketing teams
This is permission for B2B teams to stop writing thin pages for keyword variants. One thorough page about a problem will outperform six shallow pages about six phrasings of it, and it gives generative systems something substantial enough to quote.