What is TF-IDF?
TF-IDF is a formula from information retrieval that scores each word in a document by two things: how often it appears in that document (term frequency) and how rare it is across the collection the document belongs to (inverse document frequency). A word that appears often in one page but rarely elsewhere scores high; it is probably what the page is about. A word that appears everywhere, such as "the" or "and", scores near zero no matter how often it is used.
Search engines used the idea heavily in their early years, and it remains part of how text is represented in many systems. In SEO, TF-IDF tools take the top-ranking pages for a query, compute the scores for every term across them, and show which words and phrases those pages use that yours does not.
Why it matters
Why TF-IDF matters
It replaced keyword density with something less crude. Keyword density asked how many times a word appeared and encouraged stuffing. TF-IDF asks which words distinguish a page about this subject from pages about other subjects, which pushes writers toward the vocabulary a thorough treatment would naturally contain. A page about color grading that never mentions LUTs, scopes or skin tones is probably shallow, and a TF-IDF comparison against the ranking pages would show it.
The limits matter too. Modern search engines read meaning, not word counts, and a page can rank well while scoring badly on TF-IDF if it genuinely answers the question. The metric is a sanity check on coverage, not a target to optimize.
In practice
How TF-IDF works in practice
- 01
A content tool pulls the top ten or twenty results for a target query, strips them to text and computes TF-IDF for every term and phrase. It then compares a draft against that set and lists terms that competing pages use more heavily, with a suggested range. The writer reads the list as a map of the subject: subtopics the piece has not covered, terminology it uses inconsistently, questions the ranking pages all answer.
- 02
The useful response is editorial, not mechanical. A missing term usually means a missing idea, so the fix is a paragraph that covers it properly, not a sentence that drops the word in. Terms that are irrelevant to the page's angle are ignored. The draft is then checked once more, mostly to make sure nothing important was left out, and the tool is closed before it starts dictating the prose.
Watch out
What people get wrong about TF-IDF
Treating the suggested counts as targets. Hitting every term in the recommended range produces text that reads like a checklist, and search engines are good at spotting it. The other mistake is thinking TF-IDF is how Google ranks pages now. It is not; it is a decades-old statistic that still happens to be a decent proxy for whether a page covers its subject. Use it to find gaps, then write for people.
