LSI Keywords Are a Myth — Here's What Actually Helps

TL;DR: Stop adding “LSI keywords” to your content. Google does not use LSI as a ranking system, but the instinct behind the tactic is still useful if you replace synonym stuffing with intent coverage, entity coverage, and proof that your page actually answers the query.

I used to treat “LSI keywords” like a harmless shorthand. Bad term, useful instinct. Then I watched teams at mindnow turn that shorthand into a content checklist, and the pages started reading like someone had poured a keyword export into Google Docs.

Wrong — but not stupid. People search for lsi keywords because they want control. They want a list of words that makes a page feel safer. I get it. I was wrong about this for years (I liked the shortcut because it made reviews faster). But seojuice.com does not score content by asking whether you added enough fake LSI terms. It looks for missing context. That is the only part of the old LSI conversation worth saving.

LSI keywords are not a Google ranking factor

LSI stands for latent semantic indexing (an older information retrieval method). It was designed to find relationships between words in a fixed set of documents. That history matters because the SEO version of the idea drifted into something much stranger: add “related words” to a page and Google will rank it better.

Google search is not sitting there waiting for you to add “automobile” next to “car insurance” because an LSI tool said so. Modern search is shaped by semantic systems, entities, machine learning models, links, content quality, query interpretation, and user context — none of which behave like the LSI keyword folk story.

“There's no such thing as LSI keywords -- anyone who's telling you otherwise is mistaken, sorry.”

John Mueller, Search Advocate at Google, said that in 2019. It is the blunt version of the correction.

“The truth is that LSI Keywords are a myth.”

Bill Slawski, Director of SEO Research at Go Fish Digital, gave the patent-grounded version. Slawski spent years reading Google patents and information retrieval papers, which makes his pushback more useful than a generic “Google says no” argument.

Why the myth refuses to die

The term sounds technical. That helps tools sell it. It also gives writers a checklist, which is comforting when a page is underperforming and nobody wants to say, “The answer is incomplete.” Clients like it because it appears measurable. Red term, add term, green score, invoice paid.

That workflow feels productive. Often, it only makes the article heavier.

The part people get almost right

Related terms can reveal missing coverage. They are clues, not ranking tokens. Keep the research instinct. Throw away the fake mechanism.

If a tool suggests “burr grinder” for an article about coffee grinders, that might be useful. If it suggests “coffee bean machine” and no human would say it, delete it. The question is not “Did we add the term?” The question is “Did this suggestion expose something the reader expected us to explain?”

What people call “LSI keywords” are usually four different things

Bad SEO happens when four different signals get dumped into one bucket called “LSI.” A synonym, an entity, a subtopic, and a query modifier do different jobs. Collapse them into one bucket called LSI — and the page gets worse.

A synonym rarely deserves its own paragraph. “CRM platform” and “customer relationship management software” might both appear in the same article, but forcing both into every section creates robotic prose. Use the wording your buyer uses. Vary it only when the sentence asks for it.

An entity is different. If you write about CRM software and never mention Salesforce, HubSpot, or Pipedrive, the omission may make the page feel unserious. You do not need a paragraph for every brand, but the reader expects orientation. Entities help define the space.

A subtopic can be much larger. For “technical SEO audit,” terms like crawlability, indexation, canonical tags, JavaScript rendering, Core Web Vitals, XML sitemaps, and internal links are not interchangeable words. They are parts of the work. Missing one may mean your guide fails the query.

A query modifier may tell you to avoid the search entirely. If your page targets “CRM software” and the tool shows “free CRM software for nonprofits,” that is not a phrase to sprinkle into a generic article. It may be a separate page, a short note, or a bad fit.

The same applies to “coffee grinder.” “Burr grinder” is probably a required concept. “Blade grinder” is likely a comparison point. “Best coffee grinder under 100” is a buying intent modifier. “Coffee grinding machine” may be a synonym, or it may sound odd in your market. The classification matters more than the export.

The replacement: semantic coverage, not synonym stuffing

Semantic coverage means the page includes the concepts, entities, questions, constraints, examples, and proof needed for a user to trust the answer. That is the current practical version of the idea.

The common optimization process is backwards. A writer drafts a thin article, runs it through a related-keyword tool, then inserts terms wherever the score is low. That creates lumpy content: one paragraph defines the topic, the next suddenly says “latent semantic indexing” three times, then a random “keyword density” sentence appears because the editor turned the term red.

Build the coverage map before writing. If you need a broader framework, our content optimization workflow explains how to turn research into page structure instead of after-the-fact patching.

Start with the intent

For “lsi keywords,” the intent is mixed. Some readers want a definition. Some want a tool workflow. Some want confirmation that LSI still works. The page should serve all three, but it should lead with the correction. If the reader came looking for a magic list, the honest answer comes first.

That order matters. If you spend 800 words explaining how to find “LSI keywords” before saying the term is false, you have taught the wrong habit.

Identify entities

For this topic, useful entities include Google, John Mueller, Bill Slawski, latent semantic indexing, semantic search, entities, embeddings, RankBrain, BERT, and content quality. You do not force every entity into the article. You use the ones that make the explanation clearer.

John Mueller belongs because he directly rejected the concept. Bill Slawski belongs because he explains why the phrase does not match Google’s public patents and research direction. BERT may belong if you are explaining modern language understanding. A paragraph listing every algorithm name would be padding.

Entity work is about clarity, not name-dropping. If this is part of a larger SEO program, connect it to your entity SEO and topical authority process so writers know when a named concept actually earns space on the page.

Add proof, not padding

“I would say make sure that you are focusing on the content quality and that you are focusing on delivering value to your users.”

Martin Splitt, Search Developer Advocate at Google, keeps pulling the conversation back to content quality. That sounds obvious until you watch a team spend 30 minutes debating whether “semantic keywords” appears enough times in a paragraph that still does not answer the user’s question.

Proof looks like examples, source handling, screenshots where they help, product data, client experience, comparisons, and editorial judgment. At mindnow, the practical win has rarely come from adding one more adjacent phrase. It comes from finding the missing comparison, the missing constraint, or the missing proof point.

A practical workflow for replacing LSI keyword research

You can still use related keyword exports. Just stop treating them like instructions. This workflow keeps the useful research and removes the superstition.

1. Search the main query and read for intent. Do not count words. Read the top results and ask what promise each page makes. Are they defining the term, selling a tool, comparing methods, or debunking a myth?
2. Pull recurring headings, examples, objections, and entities into a working note. You are looking for the shape of the topic, not a phrase quota.
3. Separate suggestions into synonym, entity, subtopic, and query modifier. This is where the export becomes useful. A synonym may need no action. A subtopic may need a section.
4. Delete terms that only exist to satisfy a score. This is the uncomfortable step. If the phrase does not change the answer, the reader probably does not need it.
5. Turn true subtopics into headings or paragraphs. If “query modifiers” changes how someone should handle keyword suggestions, it deserves explanation. If “semantic SEO” only needs a bridge sentence, keep it short.
6. Add examples from your product, site, client work, or data. Generic pages lose because they never prove contact with the problem. Use what you have actually seen.
7. Read the page out loud and remove phrases no human would say. If I would not say the phrase in a mindnow client call, I do not want it in the article.

“I usually just use Google to look for frequently co-occurring complete phrases that rank for the same term, and I look at sources such as Wikipedia to find knowledge bases to locate domain terms.”

Bill Slawski described the safer habit: look for co-occurring phrases and domain terms. That is research. It is not a secret scoring layer.

This is also where seojuice.com fits. The tool can help surface missing internal links, thin coverage, and orphaned context between pages. It can show that your “semantic SEO” article never links to your “keyword research” article, or that five posts mention a concept without a clear hub. The writer still chooses what belongs. Software can point to the gap; it cannot decide whether the reader needed 40 words or 400.

For query modifiers, connect this work to a real keyword research process. “For beginners,” “tools,” “examples,” “free,” and “template” are not interchangeable. Each one changes the promise of the page.

Example: optimizing a page without using fake LSI keywords

Take the target of this article: “lsi keywords.”

The bad version starts with a list from a content tool: “semantic keywords,” “latent semantic indexing,” “keyword density,” “Google algorithm,” “related keywords,” “SEO content,” and “semantic SEO.” The writer has already drafted a thin post, so they sprinkle those terms into the body. The page gets longer, but not more useful.

The better version starts with a coverage map:

• Definition of latent semantic indexing
• Clear statement that Google does not use LSI as SEOs describe it
• John Mueller’s rejection of the term
• Bill Slawski’s myth explanation
• Why related terms still appear in SEO tools
• Difference between synonym, entity, subtopic, and query modifier
• Modern replacement workflow
• Example of editing a paragraph

That map gives the page a spine. Now the writer can decide what earns space. “Keyword density” may get one sentence because it belongs to the same old checklist mindset. “Entity” may need a definition. “Search intent” may need a short section. “Semantic keywords” may be mentioned only to explain why the phrase often hides the same mistake.

Before:

LSI keywords are semantic keywords related to your main keyword. Using LSI keywords in SEO content helps Google understand your page. Add related keywords, keyword density terms, and Google algorithm phrases to improve semantic SEO and make your content more relevant.

That paragraph sounds optimized only if nobody reads it closely. It repeats the myth, smears several concepts together, and gives the writer a task that makes the page worse.

After:

LSI keywords are an outdated SEO label for related terms. Google has rejected the idea that adding LSI terms is a ranking tactic, but related suggestions can still help you spot missing coverage. Sort each suggestion first: is it a synonym, an entity, a subtopic, or an intent modifier?

The second paragraph isn’t better because it contains more keywords — it’s better because it corrects the premise and gives the reader a decision method. That is the difference between phrase insertion and editorial work.

How to judge related keyword suggestions without wrecking the page

Use four questions before adding anything from a related keyword list.

Ask “does this change the answer?”

If adding the term changes the answer, it may be a subtopic. “BERT” changes an article about modern language understanding if you are explaining how search systems process meaning. It does not belong in a beginner guide just because a tool found it nearby.

Ask “is this an entity the reader expects?”

If the reader expects to see John Mueller in an article debunking LSI keywords, include him. If the tool suggests a random adjacent phrase, ignore it. Reader expectation is a better filter than term frequency.

Ask “would I say this to a client?”

This test is stupidly effective. If I would tell a client, “We need to explain the difference between related terms and ranking factors,” it can go in the article. If I would not say, “We need to add more Google algorithm semantic keyword density,” it stays out.

I still fail this test when I rush (usually when a deadline is louder than my judgment). Reading the draft aloud catches most of it.

Ask “does this deserve a sentence, a section, or deletion?”

Most suggestions deserve deletion. Some deserve one sentence. A few deserve a section. The mistake is treating every suggestion as equal because the export printed them in the same column.

Internal links belong in this judgment too. If a related term points to a page you already have, link it instead of re-explaining everything. A strong internal linking system turns related topics into a readable network rather than one bloated article.

Why this matters more in the embeddings era

Search systems are better at matching meaning, intent, and context. That does not make precision less important. It makes precision more visible.

In older SEO folklore, the page could look relevant by repeating the right neighbors. In a world shaped by entities, embeddings, passages, links, and user behavior, thin relevance is easier to spot. If the post has no original angle, no entity clarity, no brand proof, and no useful examples, adding “semantic keyword” five times will not save it.

“We're not just mechanics tweaking engines—we're engineers building the actual systems.”

Mike King, Founder and CEO of iPullRank, frames this as relevance engineering. I like that phrase because it moves the work away from folklore and toward systems: what does this page need to be trusted, connected, and selected?

“Build a notable, popular, well-recognized brand in your space, outside of Google search.”

Rand Fishkin, Co-founder of SparkToro, made that point after the 2024 Google leak analysis. The practical warning is simple: brand and entity recognition are built outside search, not just inside search. A weak page from an unknown source cannot be rescued by adjacent words.

So should you use LSI keywords?

No, not as an SEO tactic. Do not buy a tool because it promises LSI keyword optimization. Do not ask writers to hit an LSI score. Do not add awkward phrases because a content editor turned a term red.

Yes, use related terms as clues. Treat them as research inputs, not ranking ingredients. The workflow is simple: find what the page is missing, add what helps the reader, and delete the rest.

LSI keywords are the wrong map. Semantic coverage is the road.

FAQ

What are LSI keywords?

In SEO, “LSI keywords” usually means words or phrases that are related to a main keyword. The label comes from latent semantic indexing, an older information retrieval method, but the modern SEO use of the term is mostly inaccurate.

Does Google use LSI keywords?

Google representatives have rejected the idea that LSI keywords are a ranking tactic. Google uses many systems to understand queries and pages, but that does not mean it is checking whether you inserted a tool-generated list of LSI terms.

Are related keywords still useful?

Yes, if you treat them as research clues. A related keyword can reveal a synonym, entity, subtopic, or intent modifier. The value is in sorting the suggestion correctly.

What should I use instead of LSI keywords?

Use semantic coverage. Map the query intent, identify useful entities, find missing subtopics, add examples and proof, then edit out phrases that only exist to satisfy a score.

Can content optimization tools help?

They can help you spot gaps, but they should not write the page by proxy. If a tool shows a missing concept, decide whether it deserves deletion, a sentence, a section, or a separate page.

Replace the checklist with a coverage system

If your content process still asks writers to “add more LSI keywords,” replace that instruction with a coverage review. seojuice.com can help you find missing internal links, thin context, and disconnected topic clusters — but the winning move is still editorial judgment.