LLM optimization is the practice of making a brand's facts easy for a language model to retrieve, attribute and restate correctly, whether or not a search page sits in front of the answer.
This is the sense CiteAngle uses when it measures. The word travels with other meanings in this market, so we pin ours before it turns up in a report.
Where this lands in the measurement
More answers now arrive without a results page in front of them. If that answer gets your name or your category wrong, rank never enters into it.
The work lands on machine-readable facts, consistent naming across surfaces, and sources an assistant can resolve.
What it costs to leave this loose
When an answer arrives with no results page in front of it, a wrong fact about your company travels with nothing beside it to correct the record.
Machine-readable facts and naming consistency are checked across surfaces, and the disagreements are listed page by page.
Who you want measuring this
One reading does not settle a term like this. The answer an assistant writes today can name different companies tomorrow, so a Panorama round puts 50 buyer questions to every place on our US list, 7 separate times each, and reads all 17 of them — ChatGPT · Perplexity · Claude · Gemini · Grok · Google AI Overviews · Google AI Mode · Bing Copilot · DuckDuckGo AI Assist · Google Search · Bing Search · Yahoo Search · DuckDuckGo Search · Google News · Bing News · YouTube · Google Short Videos. Radar starts at 5 questions and covers the same list under the same rule.
We put the same questions to the competitors you name inside the same runs, so you see both in one table. The conditions are sealed, so you can reproduce the round and check it, and every planned observation is published with its state, failures included. When you choose who measures this, check that all three are in place.
That describes how the measuring is done. It is not a promise about where your numbers land.
Pages that use this term
Terms worth pinning at the same time