LSI Keywords: What They Are, Why the Term Is a Myth, and What to Actually Do

You have probably been told that sprinkling LSI keywords through your content will lift your rankings. Plenty of tools and blog posts still sell that idea. The problem is that the advice is built on a term Google has flatly rejected, so following it can waste hours and tempt you into keyword stuffing that does more harm than good. This guide clears it up: what people actually mean by LSI keywords, why the label is wrong, and what genuinely helps your pages rank in 2026.

What Are LSI Keywords?

LSI keywords are terms and phrases conceptually related to your main keyword that help search engines and readers understand what a page is about. Write about coffee and related terms like beans, brew, caffeine and espresso naturally appear. They add context and topical depth, not just repetition of the target phrase.

That is the working definition most SEOs use. It is also, strictly speaking, inaccurate. LSI stands for latent semantic indexing, a specific technology, and the popular idea of “LSI keywords” has very little to do with it. The rest of this guide explains the gap between the two, and what to do with that knowledge.

If you would rather hand this off, our Singapore SEO services cover the semantic side of content for you.

What Is Latent Semantic Indexing (and LSA)?

Latent semantic indexing comes from a 1990 research paper, Indexing by Latent Semantic Analysis, published in the Journal of the American Society for Information Science. The authors built it to solve what they called the “vocabulary problem”: people describe the same idea in many different words, so matching a query to a document by exact words alone misses a lot of relevant material.

The method reads a large body of text and works out which words tend to appear in similar contexts. It uses a maths technique called singular value decomposition to map those hidden relationships. You do not need the maths to use the idea. What matters is the goal: understand meaning through the company a word keeps, not just the word itself.

LSI vs. LSA: The Distinction That Trips People Up

The two terms get swapped constantly, which adds to the confusion. Latent semantic analysis (LSA) is the underlying method that analyses word relationships. Latent semantic indexing (LSI) is that same method applied to search and document retrieval.

In everyday SEO talk, people use them interchangeably and it rarely matters. If you want to be precise: LSA names the technique, and LSI names its use in indexing and retrieving documents.

How LSI Was Meant to Work (Synonymy and Polysemy)

LSI set out to fix two language problems. The first is synonymy: many different words describe the same thing. “Income” and “earnings” mean much the same, so someone searching one term should still find pages using the other.

The second is polysemy: one word carries several meanings. Take “wall street.” A system matching words alone might serve up articles about murals that happen to contain “wall” and “street.” A system that understands context knows the phrase relates to finance, because “finance,” “wall” and “street” often turn up together. That contextual read is what LSI was designed to deliver.

Is “LSI” Real? What Google Actually Uses

Here is the short answer: Google does not use latent semantic indexing to rank pages, and it has said so plainly. In 2019, Google’s John Mueller put it bluntly on Twitter: “There’s no such thing as LSI keywords — anyone who’s telling you otherwise is mistaken, sorry.”

So the technology is real, but “LSI keywords” as a ranking signal is not. Search patent researcher Bill Slawski made the same point that year, noting these terms do not use LSI and are not really keywords. The idea stuck around anyway, largely because it gave people something simple to sell.

Why Google Doesn’t Use LSI

LSI came out of the late 1980s and was built for small, fixed collections of documents. The web is neither small nor fixed. It runs to trillions of pages and changes every second, and the maths behind LSI does not scale to that.

So Google never adopted it. Instead, it built its own systems for reading meaning, which handle scale and nuance in ways LSI never could.

What Google Uses Instead

Google understands language through a stack of machine-learning systems, laid out in its own post on how AI powers search results. Four are worth knowing:

• RankBrain (2015) was Google’s first deep-learning system. It connects the words in a query to real-world concepts, so a search for “the consumer at the top of a food chain” returns pages about an “apex predator.”
• Neural matching (2018) looks at whole queries and pages rather than single words, matching the broader concepts behind them.
• BERT (2019) reads how combinations of words work together, so small words like “for” or “without” change the result. It now plays a part in almost every English query.
• MUM is a newer model, around a thousand times more powerful than BERT, though Google says it is not yet used for general ranking.

On top of these sits the Knowledge Graph, which maps entities like people, places and things and the relationships between them. None of this is LSI. All of it does the job people imagined LSI was doing.

LSI Keywords vs. Related Concepts

Part of the confusion is that “LSI keywords” gets muddled with several other terms. Sorting them out makes it much clearer what you should actually be doing. The table below sums up the differences, and the sections after it go deeper.

Term	What it is	Do you target it?	Example
Primary keyword	The main term a page is built to rank for	Yes, deliberately	“lsi keywords”
Secondary keyword	A supporting keyword with its own volume, behind the primary	Yes, deliberately	“what are lsi keywords”
Semantic / “LSI” keyword	A context term that shows topical depth	No, it appears naturally	“synonym,” “entity,” “BERT”
Synonym	Another word for the same idea	No, use for variety	“running” for “jogging”

LSI Keywords vs. Synonyms

Synonyms and semantically related terms are not the same thing. A synonym is another word for the same idea: “running” for “jogging.” A related term is something that tends to appear alongside your topic without meaning the same thing, like “shoes,” “cardio” and “5k” for jogging.

Both have their uses. Synonyms help you avoid repeating the same phrase, while related terms show real breadth of coverage. It is the related terms, not the synonyms, that people usually have in mind when they say “LSI keywords.”

LSI Keywords vs. Secondary Keywords

These two overlap, which is why the difference gets blurred. A secondary keyword is a keyword you deliberately target on the page, often one with its own search volume, sitting behind your primary keyword. A semantic or “LSI” keyword is a supporting term you weave in for context, not necessarily something you are trying to rank for.

Put simply: you choose secondary keywords on purpose to win extra searches. Semantic terms mostly turn up on their own when you cover a topic well.

Where Semantic Keywords Sit Among Keyword Types

It helps to see where this fits in the wider picture. Marketers usually sort keywords into a handful of types:

• Head (short-tail) keywords: broad, high-volume, like “shoes.”
• Long-tail keywords: longer and more specific, like “waterproof trail running shoes.”
• Primary (focus) keywords: the main term a page targets.
• Secondary keywords: supporting targets behind the primary one.
• Semantic (or “LSI”) keywords: context terms that support the topic.
• Branded keywords: those containing a brand name.
• Local (geo-targeted) keywords: tied to a place, like “SEO agency Singapore.”
• Transactional keywords: signalling intent to buy or act.
• Informational keywords: signalling a search for knowledge.

Semantic keywords are the connective tissue among these, not a separate ranking lever. You do not pick them the way you pick a primary or secondary keyword.

LSI Keywords vs. Entities (The Modern Reframe)

The concept people actually want in 2026 is entities, not “LSI.” An entity is a distinct thing Google recognises, a person, place, brand or concept, and stores in its Knowledge Graph along with how it links to other things.

When you cover a topic thoroughly and mention the right related entities, you help Google place your page in that web of meaning. That is the modern version of what “LSI keywords” was clumsily pointing at. If earning visibility in AI-driven search matters to you, our AI SEO services work at this entity level.

Do “LSI Keywords” Still Help Your SEO? (An Honest Verdict)

Yes, but not for the reason the myth claims. Including related terms does help your pages, because it signals that you have covered a topic properly. That gives Google’s language systems more to work with and answers more of what a reader came for.

What is not happening is any “LSI” mechanism rewarding you for hitting a checklist of magic words. The uplift comes from genuine topical depth, not from the label. So the practical takeaway is simple: stop chasing “LSI keywords” and start covering topics fully. The unit of optimisation is the topic, not a word list.

How to Find Semantic Keywords (The Practical Bit)

You do not need paid software to find related terms. Most of the best sources are free and sit inside Google itself. Here are eight ways to build a strong list, starting with the free ones. If you would rather see where your existing pages fall short first, our SEO audit can map that for you.

Google Autocomplete

Start typing your keyword into Google and watch the suggestions drop down. These come from real searches, so they show you the terms and phrases people actually pair with your topic. Jot down the ones that fit.

Google “Related Searches” (Bottom of the SERP)

Scroll to the foot of any results page and you will find a block of related searches. They point to adjacent subtopics and phrasings you might have missed, and they are a quick way to sense the shape of a topic.

Bold Terms in Google Snippets and Featured Snippets

Google often bolds the terms it sees as relevant in the descriptions under each result, including close variants rather than just your exact words. Search your keyword, scan the bolded words across the results, and you have a ready list of terms Google links to the topic.

People Also Ask boxes

The “People also ask” box surfaces the questions searchers have around your topic. Each question is a subtopic worth covering, and expanding one usually loads more. They are especially useful for shaping headings and an FAQ.

Google Images Topic Tags

Search your keyword in Google Images and look at the tag chips along the top. Google generates these from how the topic is described across the web, so they make a tidy source of closely related terms.

Google Keyword Planner

Google’s free Keyword Planner returns related keyword ideas once you enter a term, and you can point it at a competitor’s landing page to pull the terms Google associates with that content. You will need a Google Ads account, but you do not have to run ads to use it.

Third-Party Tools (LSIGraph, Ubersuggest, Semrush, Serpstat, KeySearch)

Several tools package related-term research into one place. LSIGraph generates related keywords and is handy for planning subheadings. Ubersuggest, from Neil Patel, produces keyword ideas with country and language filters. Serpstat offers a related-keywords report alongside search suggestions, and Semrush and KeySearch do similar work. Most have a free tier that is enough to get started. Treat them as accelerants, not necessities.

AI and LLM prompting

Large language models are a fast way to draft a related-term list in 2026. Ask a tool like ChatGPT, Gemini or Claude for the subtopics, entities and terms a thorough article on your keyword should cover. Then sense-check the output against real search demand, since a model can invent plausible terms nobody searches for. Used that way, it is a strong first-draft step rather than a final answer.

How to Use Semantic Keywords Without Overdoing It

Finding related terms is the easy part. Using them well without slipping back into keyword stuffing is where most people trip up.

Where to Place Them

Work related terms in wherever they read naturally: the title, your headings, the opening and closing paragraphs, image alt text and the body copy. Exact placement matters far less than people think. What counts is that the terms appear because you are covering the topic properly, not because you slotted them into a formula.

Don’t Keyword-Stuff or Force Terms

Cramming in every related term you found will hurt more than help. It reads badly for people and can look manipulative to Google. Read your draft back, and if a term feels bolted on, cut it. Clear, natural writing beats a high term count every time.

Match Search Intent (Informational, Navigational, Transactional)

Related terms only help if they serve what the searcher wants. Someone with an informational query is after an explanation, a navigational one is heading for a specific site, and a transactional one is ready to act. Work out which intent your page answers, then let the terms you include support that job rather than pull against it.

LSI, Semantic SEO and AI Search in 2026

The idea has come back into fashion, and it is worth understanding why. AI Overviews, ChatGPT, Perplexity and Gemini all reward content that covers a topic thoroughly and mentions the right related entities. That is the same instinct “LSI keywords” was reaching for, just described more accurately as semantic SEO.

The honest through-line is this: “LSI” was clumsily pointing at something real. Meaning beats repetition. Pages that read as complete, well-structured and genuinely useful are the ones that get cited in AI answers and win in search, so covering a topic in full now matters more than ever. This is the thinking behind our AI SEO services, which focus on topic and entity coverage rather than word lists.

Should You Pay an Agency for “LSI Keyword” Services? (Singapore Context)

If a vendor in Singapore is selling “LSI keyword optimisation” as a standalone service, treat it as a warning sign. The term has been outdated for years, and leaning on it suggests dated methods. You might be paying for a keyword-stuffing exercise dressed up in old jargon.

Good semantic SEO looks different. It means researching the subtopics and entities your audience cares about, then covering them properly with content that reads well and answers real questions. If you are weighing up proposals, ask how a provider approaches topical depth and search intent, not whether they can hand you a list of “LSI keywords.” Our Singapore SEO services take that topic-first approach, and it may be worth comparing against whatever else you are considering.

Frequently Asked Questions

What Is an LSI Keyword?

An LSI keyword is a term conceptually related to your main keyword, like “espresso” or “caffeine” for an article about coffee. The label is popular but technically inaccurate. What people mean by it is a semantically related term that helps show topical depth, which does help your content even though “LSI” as a mechanism is not something Google uses.

What Does Latent Semantic Indexing Mean?

Latent semantic indexing is an information-retrieval technique from a 1990 research paper. It analyses which words tend to appear in similar contexts across a body of text, using a maths method called singular value decomposition, to work out meaning from word relationships. It was built for small document sets, not the modern web.

Are LSI Keywords the Same as Secondary Keywords?

No. A secondary keyword is one you deliberately target, often with its own search volume, sitting behind your primary keyword. A semantic or “LSI” keyword is a supporting context term you include for topical depth, not necessarily one you are trying to rank for. They can overlap, but you choose secondary keywords on purpose while semantic terms mostly appear on their own.

What Are the Different Types of Keywords in SEO?

Common types include head (short-tail), long-tail, primary, secondary, semantic (or “LSI”), branded, local, transactional and informational keywords. They describe different roles: some are about length or volume, others about intent or ownership. Semantic keywords are the context terms that support your topic rather than a separate ranking target.

Does Google Use LSI Keywords?

No. Google’s John Mueller stated in 2019 that there is no such thing as LSI keywords. Google understands language through systems like RankBrain, neural matching, BERT and its Knowledge Graph, not latent semantic indexing. Related terms still help your content, but through topical coverage, not an “LSI” ranking signal.

How Do I Find LSI (Semantic) Keywords for Free?

Use Google itself: autocomplete suggestions, related searches at the foot of the results, the “People also ask” box, bolded terms in result snippets and the topic tags in Google Images. Google Keyword Planner is free too with a Google Ads account. These cost nothing and draw on real search behaviour.

Are LSI Keywords Still Worth Using in 2026?

The concept is worth understanding, even if the label is wrong. Covering related terms and entities helps your content in both traditional and AI-driven search. So focus on thorough topic coverage rather than chasing a list of “LSI keywords,” and you will get the benefit the myth promised without the downsides.

Conclusion

“LSI keywords” turns out to be a myth with a useful lesson buried inside it. Google does not use latent semantic indexing, and no magic word list will lift your rankings. But the instinct behind the term, that meaning and context beat blunt repetition, is exactly right and matters more in 2026 than it ever did.

So the practical move is to stop hunting for “LSI keywords” and start covering topics in full, with the related terms and entities your readers expect. Do that and both search engines and AI tools will find your pages easier to trust.

Found this useful? If you would like a hand putting it into practice, our SEO agency in Singapore is happy to help.