Google has said, at least twice and with the same clarity, that there is no such thing as an "LSI keyword". In 2019, John Mueller of the search team wrote "There's no such thing as LSI keywords" and added that anyone claiming otherwise is mistaken. When the question came up again four years later, his answer had not changed: they have no effect, and whoever recommends them is still wrong after all these years. Google's own descriptions of its systems mention the synonym system, RankBrain, neural matching and BERT; latent semantic indexing does not appear.
The real issue is not that the term is wrong. It is that the wrong term hides a real need. People search for the same thing using different words, and if a page matches none of those words, it cannot be found. This problem is real, measured and old. But the advice to "sprinkle 20 LSI keywords into your content" does not solve it; it only gives the impression of solving it.
What LSI actually did
Latent semantic indexing was not a search engine tactic but a mathematical solution to a specific information retrieval problem. A team at Bell Communications Research filed a patent application for the technique in September 1988, and a detailed description of the method was published two years later in an academic paper titled "Indexing by Latent Semantic Analysis".
The method works like this. A document collection is turned into a huge table of numbers whose rows are terms and whose columns are documents. This table is reduced to roughly 100 orthogonal factors using singular value decomposition. After the reduction, each document is represented by a vector of about 100 numbers. The user's query is also converted into a pseudo-document vector in the same space, and documents that sit close enough to this vector are returned as results.
term-document matrix (the MED test in the paper)
5823 rows (terms) x 1033 columns (documents)
reduction with SVD
each document -> a vector of about 100 numbers
each term -> a single point in the same space
query -> pseudo-document vector in the same space
similarity = cosine between two vectorsThe technique was trying to solve two problems. The first is synonymy: there are many ways to describe the same thing, and the word a user chooses may not match the word a document uses. The second is polysemy: the same word refers to different things in different contexts. The paper gives a concrete measurement of how serious the synonymy problem is. Two people choose the same main keyword for a single, well-known object less than 20 percent of the time. This is the real basis for the advice to "cover related concepts", and the advice itself is sound. What is wrong is believing that this need is called LSI.
The technique's limits were written down by its own authors
Reading the original study makes it obvious why the term could never become a web ranking factor. The method was tested on two standard collections: a set of 1,033 medical abstracts and a second set of 1,460 information science abstracts. These are fixed, closed collections of a few thousand documents.
The paper itself records the scaling problem. Searching in a high-dimensional space does not run efficiently on serial computers, which makes the method less desirable for very large collections. The update side is even more critical: the initial decomposition is time-consuming, and how many new terms and documents can be added without recomputing it is left as an open question in the paper. For a web index that changes constantly and produces new pages and new terms every day, these two limits alone are decisive.
The results were mixed, too. On the medical abstracts set, LSI outperformed plain term matching. On the information science set, it did not beat plain term matching. On polysemy, the method offered only a partial solution, and the paper states the reason clearly: because each term is represented by a single point in the space, a word with several meanings is reduced to a weighted average of its meanings. If none of the real meanings resembles that average, serious distortion results.
In other words, even in its own paper LSI is not presented as "a meaning engine that solves every problem". It is described as a method that improves the synonymy problem in collections of a certain size, struggles with polysemy and is costly to scale.
What vector similarity tells us
The second concept in the title is real and still valid, independent of the myth. A vector is a representation that turns a text or word into a list of numbers. These numbers determine a position in a space, and expressions used in similar contexts end up positioned close to each other. Closeness is measured by the cosine of the angle between two vectors. Up to this point, the logic is the same in LSI and in today's systems.
The difference lies in how the space is built. LSI produces a fixed space through a one-off decomposition, and in that space each word has a single position. Whatever the sentence, the Turkish word "yüz" (which can mean "face", "hundred" or "to swim") stays at the same point. The approach Google described when introducing BERT is exactly the opposite: words are not processed one by one in order, but in relation to all the other words in the sentence. As a result, the same word receives a different representation depending on the sentence it appears in. In describing this change, Google emphasized that users would no longer have to resort to the artificial way of phrasing ("keyword-ese") they had adopted to make themselves understood by the system.
The practical consequence is this. Vector similarity is not a score that rises when certain words are added to a page. It is a holistic representation of what a text is about. What changes that representation is not a word list but what the text actually explains. For all of these concepts in one place, see our AI and SEO glossary.
Separating myth from reality
| Common claim | Verifiable status | What it means in practice |
|---|---|---|
| Google uses LSI | Google's own description of its systems includes the synonym system, RankBrain, neural matching and BERT, not latent semantic indexing | Stop optimizing for a system name and look at the problem itself |
| There is a type of keyword called LSI keywords | Google has stated explicitly on two separate occasions that no such concept exists | Think in terms of the topic's real subconcepts, not "LSI keywords" |
| The more related terms you add, the better | There is no verified finding linking the amount added to rankings | Increase the number of questions answered, not the number of terms |
| LSI is an advanced technology that decodes meaning | It was designed for collections of a few thousand documents and offers a partial solution to polysemy | Do not equate the technique with modern systems for understanding meaning |
| The list from an LSI tool is the terms Google uses | These tools have no access to Google's index or to any decomposition of it | Treat the list as a source of ideas, not a checklist |
What an "LSI keyword tool" is really selling
None of these tools runs an actual latent semantic indexing analysis. To do so, they would need access to Google's document collection and to a decomposition performed on that collection; neither is possible. The list they produce is compiled from observable surfaces: autocomplete suggestions, related searches, questions users ask and terms that co-occur on top-ranking pages.
That output is not useless. On the contrary, it is a quick way to see which subquestions a topic breaks down into. The problem is the label, and the way of using the list that the label encourages. The name "LSI" lends the list an algorithmic legitimacy, and that legitimacy turns the list into a checklist to be filled in. The usual result is that terms bringing no new information are sprinkled into the text, and the page gets longer, not more comprehensive.
The wrong term hides the right need
Rejecting the myth does not mean rejecting the observation behind it. The finding that two people choose the same word for the same object less than 20 percent of the time still holds. It is also why Google describes its synonym system as a separate component: the system needs to see that a user searching "change laptop brightness" and a manufacturer writing "adjust laptop brightness" are talking about the same need.
The logic here differs from the logic the myth sets up. Covering related concepts works not because it raises a score but because it genuinely makes the page the answer to more questions. If a page about crawl budget never mentions Googlebot, robots.txt or crawl rate, what is missing is not a term but a topic. The term is the symptom of the gap, not its cause.
What to do instead of an LSI list
- Map out the real spread of the question. Gather the subquestions behind the main query: definition, how it works, when it is not suitable, alternatives, common mistakes. Let this spread, not a word list, determine your coverage decisions.
- Meet the intent. Two users searching for the same word may want different things. Treat search intent alignment as a separate task to clarify which intent the page answers.
- Use synonyms without forcing them. If a concept's equivalents in two languages (for example Turkish and English) come up naturally in the text, let them. There is no known benefit to cramming both in at the cost of a broken sentence.
- Spread coverage across a cluster, not a single page. Instead of piling every subtopic of a subject onto one page, decide which subtopic deserves its own page. The framework for that decision belongs to topical map planning.
- Put every addition through an information test. If a term added to the text does not bring a new fact, distinction, exception or example to the sentence, remove it. If the number of terms goes up but the information does not, what you are doing is not coverage but padding.
The term's particular problem in Turkish
Most Turkish-language sources still define LSI as "the method Google uses". Because this definition dominates the corpus, summaries and AI answers generated from that corpus inherit the same mistake. This is why correcting the term is harder in Turkish than in English: the number of sources making the correction is tiny compared with the number repeating the myth.
There is a second oddity. In Turkish, the query "LSI nedir" (what is LSI) does not belong to a single information need. The same abbreviation describes the protection functions of circuit breakers in electrical engineering (long, short, instantaneous), and a manufacturer's technical page ranks among the SEO content for this query. Actual demand for the term on the SEO side is quite small.
The practical consequence is clear: it makes no sense to turn "LSI keyword" into a service, a technique or a page target. Knowing the concept is enough to recognize bad advice when it reaches a client or your team. For anyone looking for the basic framework of SEO, the right starting point is what SEO is, not LSI.
Frequently Asked Questions
Should I never use the list an LSI keyword tool gives me?
Use it, but know what it is. That list is a pool of ideas compiled from autocomplete, related searches and terms that co-occur on top-ranking pages. It is a quick way to see which subquestions a topic breaks down into. It does harm when used as a quota to be stuffed into the text.
Are TF-IDF and LSI the same thing?
No, but they are related. TF-IDF is a scoring method that calculates the weight of a term in a document and can be used to fill the cells of a term-document matrix. LSI is a separate step that reduces the whole matrix to fewer dimensions. TF-IDF sits on the input side; LSI sits on the reduction side.
Does adding synonyms improve rankings?
There is no verified finding linking the act of adding them to a ranking increase. What is verified is this: users search for the same concept with very different words, and search systems run a separate synonym layer to match these different expressions. So it makes sense for a text to naturally contain more than one way of phrasing things, not to force a word list into sentences.
What is the difference between embeddings and LSI in one sentence?
LSI gives each word a single position through a one-off decomposition of a fixed matrix, while contextual embeddings give the same word a different position depending on the sentence it appears in.
Does the same logic apply to AI answers?
The basic logic is the same: a text enters the candidate pool because it genuinely contains the answer to the query, not because certain terms have been sprinkled into it. However, different AI systems do not behave identically when selecting sources, so it would be wrong to treat an observation on one platform as a general rule.
