LSI Keywords for SEO: How to Use Them

Why the term is still everywhere

Many believe that LSI keywords can be very beneficial for SEO if mastered correctly. Whether it’s true or not, each webmaster should have an arsenal of ranking strategies that’s as vast as possible. We’ll explore LSI and its impact on the rankings in this article.

The short version: the label is wrong, but the habit behind it is right. Google does not run Latent Semantic Indexing over the web, so “LSI keywords” is a name the SEO industry invented for something else. Covering the vocabulary a topic genuinely uses still helps, because that is how modern retrieval models decide what a page is about. What follows is the history, what actually replaced LSI, and a repeatable process for choosing related terms without stuffing them into your text.

LSI: Latent Semantic Indexing

According to the SEO community, LSI keywords are phrases or single words that Google considers semantically-related to a certain subject. However, John Mueller from Google admits that there’s no such thing as LSI keywords.

To clear up this confusion, we first need to explore Latent Semantic Indexing (LSI). This is a processing technique from the ’80s. You don’t need to understand the backend of LSI to have a solid idea of how this concept works in action.

Sometimes, the information that you’re looking for is not indexed under the same keyword that you’re using for the search. Take “fall” and “autumn” for example. If you’re searching for the autumn season by using the keyword “fall” then, you don’t want to be showcased pictures of people falling. It is the search engine’s job to figure out this difference.

Synonyms and Polysemic Words

We all know that a synonym is a word that represents the same basic thing as another word. You can take fall and autumn, or rich and wealthy as an example.

Let’s say that we have two web pages about the topic cars but one uses the classic word “cars” and the other one “automobiles”. An ancient search engine will only display the first page when you’re searching for the keyword “cars” when both pages should be showcased.

The story with polysemic words is not much different either. They are words that mean two different things. “Mouse” can refer to the rodent or the computer hardware, the same as “apple” may refer to the fruit or the tech giant.

If you’re searching for “Apple computers” then you’d want the search engine to showcase only results relevant to the company. That’s why search engines need to understand the different facets of polysemic words.

How does LSI work?

Computers can’t understand the fundamental relationships between certain words in the manner that humans do. They can’t understand that fall and autumn are the same things, the same way they don’t know that Mick Jagger was part of Rolling Stones.

They don’t have this knowledge and, you can’t input it. It would simply take too long to do it.

Now, LSI, on the other hand, is using mathematical formulas to create relationships between keywords. If we’re using LSI to scan some documents about the automotive industry, the algorithm will likely figure out that cars are the same as automobiles.

A worked example: how LSI spots a synonym

The mechanism is easier to trust once you see it in miniature. Imagine a tiny index of four documents. Count how often five terms appear in each one and you get a term-document matrix like this.

Document	car	automobile	engine	repair	market
D1 – Car engine repair guide	4	0	5	6	0
D2 – Automobile engine rebuild costs	0	5	6	3	1
D3 – Used car market report	6	0	1	0	5
D4 – Automobile market share by brand	0	4	0	0	6

Notice that “car” and “automobile” never appear in the same document. A literal keyword matcher would treat them as unrelated strings. But both of them keep company with the same neighbours: engine and repair on one side, market on the other. LSI factorises this table, keeps only the strongest few dimensions and discards the rest as noise. In that reduced space the two columns nearly overlap, so a query for “car engine” can surface D2 even though D2 never uses the word car. Nobody taught the system that the two words are synonyms; the co-occurrence pattern implied it.

The catch is the arithmetic. Four rows are trivial. A web-scale collection of billions of pages and millions of distinct terms is not, and the decomposition has to be recalculated whenever the collection changes — which, on the web, is constantly. That is the wall LSI runs into.

Google and LSI

Although people rightfully assume that Google is using LSI, certainly, they are not. The engine can clearly understand how synonyms and polysemic words work but, Google is not using LSI.

Google representatives have stated multiple times that the search engine is not making use of this technology. There’s a couple of reasons that further strengthen Google’s decision not to use LSI.

LSI is outdated

The technology was invented in the ’80s, before the creation of WWW and the internet as we know it. It was only intended to be applied for scanning small documents, and not immense databases like the internet.

LSI simply put can’t handle the internet. Google has developed a more effective and scalable solution to handle this problem. It’s using word vector approaches that scale tremendously better and work on the open web. LSI can’t stand a chance against Word2vec — and Word2vec itself is now the old part of the story, replaced in production by transformer models that read a word in the context of the sentence around it.

LSI is patented

Latent Semantic Indexing was patented by Bell Communications Research, Inc back in 1989. The patent expired in 2008, after 20 years. Google proved a solid understanding of language complexities way before 2008.

Google has been showcasing relevant results for synonyms since 2003. They have never used LSI and simply calling their technology LSI would be misleading.

What Google uses instead

If you want a mental model that is still accurate in 2026, forget the 1980s matrix and think of a chain of increasingly context-aware models. Each step solved a limitation of the previous one.

Approach	Roughly when	What it models	Web scale?
Latent Semantic Indexing / Analysis	Late 1980s	Term co-occurrence inside one fixed document collection	No — the whole matrix has to be recomputed as the collection grows
Word embeddings (Word2vec and relatives)	From 2013	One vector per word, learned from very large text corpora	Yes, but each word gets a single fixed meaning
Transformer models in Search (BERT and successors)	In Google Search from 2019	The meaning of a word inside its sentence, so word order and prepositions matter	Yes
Passage-level ranking	Announced 2020, rolled out from 2021	Individual passages within a long page, not just the page as a whole	Yes
Multimodal, multilingual models (MUM and what came after)	2021 onward	Meaning across languages and formats, and the generative answers layered on top of results	Yes

The practical consequence for a writer is the same at every step of that chain. You are not feeding a string matcher. You are giving a language model enough context to place your page confidently on a topic — and the same applies to the generative answers that now sit above the classic ten blue links, because those are assembled from passages the system already understood.

Can related keywords help rankings?

Most people consider LSI keywords as being just a bunch of related words and keyphrases. Even if this is technically inaccurate, using related words in your articles can improve your ranking.

When you’re searching for the word “dogs” you certainly don’t want to find a page that’s only including the word “dogs.” You want something relevant to the topic. So, algorithms also take into considerations synonyms, pictures and videos to assess the relevance of your page.

These related keywords and keyphrases allow Google to have a better understanding of your article’s topic. The search engine is using this information to determine the rankings in the SERPS.

Be careful about the direction of causation, though. Covering a subtopic properly is what helps; typing the word that names it is only the visible side effect. A page that mentions “anchor text” once in a sentence that says nothing has not covered anchor text. A page with three paragraphs and an example has, and it will contain the phrase naturally as a by-product. This is exactly the distinction Google draws in its guidance on creating helpful content.

How to find related keywords

If you’re familiar with the topic of your article, then you can come up with related words naturally. It would be hard to talk about link building without mentioning certain words like dofollow, nofollow and UGC.

However, even if you know your topic, there’s still a chance that you might forget including some fundamental keywords. To make sure you always include related words, here’s a list of ways to find them.

Check autocomplete results

If you want to have a better understanding of your topic, take a look at the autocomplete queries from Google search. They don’t show related important related keywords all the time but they can help you find a few essential subjects that you might want to cover in your articles.

A trick worth two minutes: type your keyword followed by a single letter, a to z, and note what the suggestions change into. The alphabet sweep exposes modifiers — comparisons, prices, alternatives, “vs”, “without” — that you would never have guessed from the bare query.

Check related searches in SERP

You can find plenty of related search queries at the bottom of the SERPs. They are similar to autocomplete results and they can give you some clues about your topic. You can find plenty of relevant keyphrases for your content.

The same page usually offers two more free sources. The People Also Ask box gives you questions phrased the way searchers phrase them, and expanding one entry loads more, so a few clicks map out an entire question tree. The “people also search for” panel that appears when you bounce back to the results shows what the previous visitor tried next, which is often the sharper signal of what your page failed to answer.

Read the vocabulary of the pages already ranking

Open the top five results for your target query and copy out only their H2 and H3 headings into one document. Delete brand names and anything that is pure navigation. What is left is a rough consensus of the subtopics that the query is understood to include. If four out of five competitors dedicate a section to something and you don’t mention it at all, that is a genuine gap rather than a term you forgot to sprinkle in.

Mine Search Console for terms you already half-rank for

For a page that has been live for a while, Google Search Console is more useful than any generator, because it reports queries Google itself already associates with your URL. The procedure takes about ten minutes.

1. Open the Performance report and set the date range to the last three months.
2. Add a filter for the exact page URL you are working on.
3. Switch to the Queries tab and enable the Position and CTR columns.
4. Export the table to a spreadsheet and sort by impressions, descending.
5. Keep the rows with meaningful impressions and an average position roughly between 8 and 30 — those are queries Google considers you a candidate for, without being convinced.
6. Use your browser’s find function to check which of those queries do not appear anywhere in your page text.
7. Whatever survives that filter is a documented gap, not a guess. Decide for each one whether it deserves a sentence, a paragraph or its own section.

Use a tool

You can find countless of “LSI keyword generators” on the web. Although they have nothing to do with LSI, they can give you a few relevant results or maybe some ideas. They may not always work as intended but they can represent a plan B anytime.

Judge these tools by what they actually do rather than by the label. Most of them scrape the current results for your query and return the terms that appear most often across the ranking pages. That is a useful brainstorm and a terrible checklist. Treat the output as candidates for research, never as a quota to hit.

A workflow for putting related terms into a page

Order matters here. Research first and write second produces a page assembled around a word list, and it reads like one. This sequence keeps the writing in charge.

1. Write the first draft from what you know, with every research tab closed.
2. State in one sentence the question the page answers. Everything below gets judged against that sentence.
3. Now collect candidates from the four sources above: autocomplete, related searches and People Also Ask, competitor headings, and Search Console if the page has history.
4. Collapse the raw list into concepts rather than strings. “Guest post”, “guest posting” and “guest blogging” are one concept, not three items to place.
5. Assign each surviving concept a destination: a new heading, a paragraph inside an existing section, a row in a table, an FAQ answer — or nothing.
6. Write the additions as normal sentences. If a term only fits by force, it does not belong on this page; it may deserve a page of its own.
7. Check the title, the H2s and the opening 100 words separately. Those positions carry the most weight for a human skimming, and clarity there is worth more than density anywhere else.
8. Read the finished page aloud. Anything that sounds like a list of synonyms gets cut.

How to decide which related terms deserve space

Five questions settle almost every case. If a candidate term fails two of them, leave it out.

Question	Add the term when…	Leave it out when…
Would a knowledgeable reader expect it?	It names a subtopic they would look for	It only ever showed up in a tool export
Can you say something specific about it?	You have at least two sentences of substance	All you can do is repeat the phrase
Does it change the answer the page gives?	It adds a condition, an exception or a step	It restates a point you already made
Does it match how your audience speaks?	Clients and customers use that wording	It is machine-generated phrasing nobody says
Would deleting it leave a hole?	The page would be visibly incomplete	No reader would ever notice

Worked example: a page about guest posting

Take a page targeting “guest posting”. Here is how the related vocabulary maps onto real sections instead of being scattered through the prose.

Related concept	Where it belongs	What it has to actually say
dofollow and nofollow	A section on link attributes	Which attribute a placement carries, and why that determines the value of the whole exercise
ugc and sponsored attributes	Same section	When a publisher is expected to apply them and what that means for you
anchor text	Its own subsection	How the anchor is chosen, and why exact-match anchors repeated at scale look manufactured
outreach	The process section	How a publisher is approached and what a pitch that gets answered contains
editorial guidelines	The vetting section	The checks that separate a real publication from a page built to sell links
topical relevance	Selection criteria	Why a link from a site covering the same subject is worth more than a bigger irrelevant one
link spam policies	The risk section	What Google’s spam policies say about links exchanged for payment or goods

Every one of those rows is a commitment to write something. That is the difference between semantic coverage and keyword decoration: the first one costs you effort, the second one costs you a find-and-replace.

Four ways people waste this technique

• Synonym stuffing. Cycling through “cars / automobiles / vehicles / motor vehicles” in consecutive sentences to hit them all. Modern models already treat these as one concept, so the only measurable effect is worse writing.
• The footer term dump. A paragraph of loosely related phrases at the bottom of the page, or hidden in a collapsed block. This is a spam pattern, not an optimisation.
• Chasing a content score. Editing sentences until a third-party tool turns green. That number reflects the tool’s sample of competitor pages, not anything Google computes.
• Terms without substance. Adding a heading for a subtopic and then writing two vague sentences under it. An empty section is worse than no section, because it invites a comparison you will lose.

FAQ

Are LSI keywords a Google ranking factor?

No. Google representatives have stated plainly that LSI keywords are not a thing. Semantic relevance is real and it matters; the specific technique named LSI is not what produces it.

How many related terms should a page contain?

There is no number, and anyone offering one is selling something. The right question is whether every subtopic a reader expects has been genuinely addressed. Cover those, and the vocabulary takes care of itself.

Does keyword density still matter?

No. Density was a proxy for topical relevance in an era of literal string matching, and it stopped being informative long before transformer models entered Search. Repetition beyond what reads naturally buys nothing.

If Google understands synonyms, do I still need the exact keyword?

Usually yes, in the places that set expectations — the title, the first paragraph, the relevant heading. Not because the algorithm cannot handle a synonym, but because a searcher scanning results wants to see their own words. Where search demand differs sharply between two synonyms, match the wording your audience actually types.

Should I go back and add related terms to old posts?

Only where the Search Console procedure above shows a documented gap. Rewriting a page that ranks well to sprinkle in extra terms risks more than it gains. Prioritise pages sitting on the second page for queries they never explicitly answer.

Do related terms help with AI-generated answers too?

The same discipline applies. Generative answers are assembled from passages, so a page with clearly bounded sections that each answer one thing is easier to quote than a long undifferentiated block. Clear headings and a direct first sentence per section are the practical version of this.

Conclusion

Although very popular for SEO, LSI keywords are not a real thing. But, as a concept, they exist in the form of related words and phrases that can boost rankings in certain cases.

Make sure to use them in context if you want to be as effective as possible. Don’t just spread them randomly across your text, use them where it makes sense.

If you want a single rule to take away: stop thinking in terms and start thinking in questions. Write down every question a reader could reasonably bring to the page, answer the ones you are qualified to answer, and let the vocabulary follow. That is the version of “LSI keywords” that survives contact with how search engines actually work — and it is the same principle behind Google’s own SEO starter guide.