Search for advice on content optimization and you will run into LSI keywords within about thirty seconds. Tools sell them. Plugins score you on them. Countless guides tell you to sprinkle them through your copy for better rankings. There is one complication: Google has stated directly that it does not use LSI, and the technology the term refers to was never designed for web search in the first place. The underlying advice is not entirely useless — it is just built on a foundation that does not exist. Here is the real story, and what to do instead.
What LSI Actually Means
Latent Semantic Indexing is a real information retrieval technique. It was patented in 1988 by researchers at Bell Communications Research, and it predates the commercial web.
The method works by building a large matrix of which terms appear in which documents, then applying a mathematical technique called singular value decomposition to reduce that matrix to a smaller set of underlying dimensions. The result is that documents about similar concepts cluster together even when they do not share exact vocabulary. A system using LSI can recognize that a document about “automobiles” relates to a query about “cars” without anyone explicitly mapping the synonym.
That was a genuinely important idea. It was also designed for small, static document collections — think a corporate archive of a few thousand documents. The computation required scales badly, and rebuilding the model as documents change is expensive.
Why Google Does Not Use It
The web is not a small, static collection. It is hundreds of billions of documents changing constantly. LSI as originally formulated cannot operate at that scale, and it was superseded long ago by approaches that can.
Google’s own people have addressed this repeatedly and bluntly. John Mueller stated plainly that there is no such thing as LSI keywords and that anyone claiming otherwise is mistaken. Google’s search advocates have made the same point across years of Q&A sessions.
What Google actually uses is a lineage of natural language systems that are far more capable:
- RankBrain (2015) — machine learning applied to interpreting unfamiliar queries
- Neural matching (2018) — connecting queries to concepts rather than to exact strings
- BERT (2019) — transformer-based models that read words in the context of the words around them
- MUM (2021) — multimodal, multilingual understanding across formats
These share LSI’s goal — understanding meaning rather than matching strings — while using entirely different mathematics. Calling them LSI is a bit like calling a modern smartphone a telegraph because both send messages.
So Why Does the Myth Persist?
Three reasons, and they are worth naming because they explain a lot of bad SEO advice generally.
It sounds technical. “Latent Semantic Indexing” carries the authority of a real academic term. Advice framed with it sounds more rigorous than “use related words,” even though that is what it amounts to.
Tools monetize it. A number of products sell “LSI keyword generators.” What they actually produce is a list of terms that co-occur with your keyword across the top-ranking pages — which is a genuinely useful output, just not LSI. The label sells better than the accurate description.
The underlying advice partly works. This is the important one. If you follow LSI advice and add semantically related terms to your content, your content usually does get better. Not because Google runs LSI, but because covering related concepts is what thorough writing looks like. The advice produces results through a mechanism completely different from the one it claims.
That last point is why the myth has been so durable. People try it, it works, and they conclude the explanation must be right.
What Actually Matters: Topical Coverage
Strip away the terminology and the useful principle underneath is this: pages that cover a topic comprehensively rank better than pages that repeat one keyword.
Google’s language models are looking at whether your page addresses the concepts a searcher on that query needs. A page about espresso machines that never mentions grind size, pressure, or milk frothing is incomplete regardless of how many times it says “espresso machine.” The related vocabulary shows up because you covered the subject, not the other way around.
This is the same principle behind targeting one topic per page rather than a list of keywords — a point we cover in how many keywords to target per page.
How to Find Genuinely Related Terms
You do not need an LSI tool. These methods surface the same information more honestly:
Read the top-ranking pages. Take the top five results for your target query and note the subtopics each one covers. Anything that appears in most of them is something searchers expect. Anything missing from all of them is a potential gap.
Mine the SERP itself. People Also Ask boxes, related searches at the bottom of the page, and autocomplete suggestions are Google telling you directly what else people want to know about this topic.
Use your keyword research output. The long-tail variants and questions you gathered during research are your subtopic list. If you built that list properly, you already have what an LSI tool would sell you. Our guide on how to do keyword research covers the process.
Check Search Console. For pages already live, the Performance report shows every query bringing impressions. Queries you rank for at position 15 with meaningful impressions are subtopics worth expanding. Our Google Search Console tutorial covers where to find this.
Ask a subject expert. Or be one. The fastest way to write comprehensive content is to know the subject well enough to anticipate what someone will ask next.
What to Do With Related Terms Once You Have Them
Here is where a lot of advice goes wrong. The instruction to “include LSI keywords” implies insertion — find gaps in your text and drop terms in. That produces awkward writing and no ranking benefit.
The better approach is structural:
- Turn major related concepts into H2 or H3 sections, so the page genuinely covers them
- Let the vocabulary appear naturally while writing those sections
- Do not force a term in if the section does not need it
- Never keep a list open and check terms off as you insert them
If a related term does not fit anywhere naturally, that is usually a signal it belongs on a different page, not that your writing needs adjusting.
The placement fundamentals still apply — titles, headings, and the rest of the on-page layer, which we cover in our on-page SEO checklist.
The Practical Test
Before publishing, ask: would a knowledgeable person reading this page feel their question was fully answered, or would they need to search again?
If they would search again, identify what they would search for, and cover it. That is the entire mechanism the LSI myth is gesturing at, minus the mythology.
A useful corollary: if you find yourself worrying about whether you have enough related keywords, you are probably optimizing at the wrong level. Worry about whether you have covered the subject. The vocabulary follows.
Drop the Term, Keep the Habit
LSI keywords do not exist as an SEO concept. Google does not use Latent Semantic Indexing, has said so directly, and uses substantially more advanced systems that work differently.
But the behavior the myth encourages — researching related concepts, covering subtopics, writing comprehensively instead of repetitively — is genuinely good practice. You can keep all of that and discard the explanation. In fact you should, because the accurate framing leads to better decisions: it points you toward covering topics, while the LSI framing points you toward inserting words.
One of those produces content worth ranking. The other produces content that reads like it was assembled from a checklist.
If you want content built around genuine topical depth rather than keyword insertion, our SEO services cover strategy, research, and execution. Reach out to the team at blogthememachine.com, and subscribe to our newsletter below for more myth-free SEO guidance.
