What are LSI Keywords? How to Use Them in SEO

You've probably heard the term thrown around in SEO circles. LSI keywords. Some people swear by them. Others say they're a myth. The truth? It's somewhere in the middle, and understanding that distinction will make you a significantly better content writer and SEO strategist.

This guide breaks it all down. What LSI keywords actually are, whether they still matter in 2026, how to find them, and how to use them without overdoing it.

Let's get one thing straight before anything else. The term "LSI keywords" is technically a misnomer, but it's a useful one, and that's why it's stuck around.

LSI stands for Latent Semantic Indexing. It's an older information retrieval method that was developed in the late 1980s. The idea was that by analyzing patterns in how words appear together across large bodies of text, you could figure out which words are conceptually related, even if they don't share the same letters.

The Origin of LSI

LSI was originally built to help computers understand documents better. Think of it like this: if you've got a document about "apple," the surrounding words tell the system whether you mean the fruit or the tech company. Words like "orchard," "harvest," and "cider" point one direction. Words like "iPhone," "MacBook," and "iOS" point the other.

The mathematical model behind LSI uses something called singular value decomposition. Don't worry, you don't need to understand the math. What matters is the concept: words that appear in similar contexts tend to have similar meanings.

So when people started talking about "LSI keywords" in SEO, they were borrowing this idea. They were saying that Google uses something like LSI to understand the full context of a page, not just the exact keywords on it.

LSI Keywords vs. Semantic Keywords

Here's where it gets interesting. Google has said, explicitly and repeatedly, that it does not use LSI. Gary Illyes from Google's Search team confirmed this publicly. So calling them "LSI keywords" isn't technically accurate, but does that mean the concept is useless? No. Not even close.

What Google does use is far more advanced. It's built on neural networks, natural language processing, and models like BERT and MUM. These systems understand language in a way that's far more sophisticated than the original LSI algorithm, but the outcome is similar in practice: Google reads your content and tries to understand what it's really about by looking at all the words on the page, not just the target keyword.

So when SEOs say "LSI keywords," they really mean semantically related terms. Words, phrases, and concepts that naturally appear in well-written content about a given topic. The label might be imprecise. The strategy behind it isn't.

A Simple Example to Make It Click

Say your target keyword is "coffee brewing." An article that only repeats "coffee brewing" over and over looks shallow. An article that also mentions grind size, water temperature, bloom time, French press, pour-over, and extraction will naturally signal to Google that you actually know what you're talking about.

That's the real value of so-called LSI keywords. They're the vocabulary of your topic, and when you speak that vocabulary fluently, search engines (and readers) trust you more.

Short answer: yes, but not in the way most outdated guides will tell you.

The longer answer requires understanding a bit about how search has evolved, and it's evolved a lot.

What Google Says (and What It Actually Does)

Google doesn't use LSI. We've established that, but Google absolutely does analyze the full semantic context of your content. BERT, which rolled out in 2019, was a turning point. It allowed Google to understand the relationship between words in a sentence, not just the words themselves. MUM went even further.

In 2026, Google's understanding of content is deep. It can tell the difference between a page about "jaguar the car" and "jaguar the animal" without you spelling it out. It rewards pages that cover a topic thoroughly. It penalizes thin content that targets one keyword and ignores everything else.

So yes, semantically related terms matter. They're part of what makes content thorough. They help search engines confirm what your page is about. They reduce ambiguity, and they naturally emerge when a human expert writes about a topic they actually know.

The Real Reason Related Terms Help You Rank

it's not just about tricking an algorithm. When you include relevant related terms in your content, you're also serving readers better. You're covering subtopics they care about. You're answering follow-up questions before they have to Google them separately.

That behavior, people spending more time on your page, reading more, bouncing less, signals to Google that your content is high quality. So the ranking benefit from semantic terms comes from two directions at once. The algorithmic side and the human behavior side.

That's a powerful combination.

When LSI Thinking Gets You in Trouble

Where people go wrong is treating related terms like a checklist. They find 20 "LSI keywords" from a tool, then try to stuff them all in. That produces robotic, bloated content. Readers can feel it, and honestly, so can Google.

The better mindset is this: write like an expert. If you know your topic deeply, the relevant vocabulary will appear naturally. Then you can review your draft and spot any obvious gaps. That's a healthy way to think about it.

You don't need to spend a fortune to find good related terms. Some of the best sources are sitting right in front of you every time you search Google.

Google's Own Features Are Your Best Friend

Start with the basics. When you type your target keyword into Google and look at the search results page, you'll find related terms hiding in plain sight.

• Autocomplete suggestions: Start typing your keyword and see what Google suggests. Those suggestions reflect real user search patterns.
• People Also Ask: These are actual questions real people search. They're gold for finding subtopics and related terms.
• Related searches: Scroll to the bottom of the results page. The suggested searches there reveal conceptually connected queries.
• Bold text in snippets: Google bolds words in meta descriptions that match the user's query, including synonyms and related terms.

Spend ten minutes doing this for any topic and you'll have a solid list to work from.

Free Tools Worth Bookmarking

Beyond Google itself, a few free tools can speed this up considerably.

• LSIGraph: Enter your main keyword and get a list of related terms. Free tier is limited but useful for quick research.
• AlsoAsked: Pulls People Also Ask data at scale. Great for finding question-based related terms.
• AnswerThePublic: Visualizes all the questions and prepositions associated with a keyword. Excellent for spotting content gaps.
• Google Search Console: Look at the queries bringing traffic to an existing page. You'll often find related terms you hadn't thought of.
• Semly Pro's free tools: Semly Pro offers AI-assisted content research tools that surface related terms and semantic gaps in your draft before you publish.

Paid SEO Tools That Go Deeper

If you're doing this at scale or for client work, paid tools save a significant amount of time. Here's what's available and what each does well.

• Semly Pro: Generates long-form SEO articles that already incorporate semantically relevant terms. The AI visibility score helps you see how well your content covers a topic. Available from €139/mo for solo marketers.
• Ahrefs: Keyword Explorer shows "also rank for" and "also talk about" reports that surface naturally related terms from top-ranking pages.
• Semrush: The SEO Writing Assistant flags missing semantically related terms in real time as you write.
• Surfer SEO: Analyzes top-ranking pages and tells you which terms they include, how often, and how your draft compares.
• Frase: Pulls related questions and topics from search results and competitor pages, then lets you write directly in the tool.

Each of these works. The right choice depends on your budget and what else you need from the tool beyond related-term research.

Finding related terms is only half the job. You need to know how to use them without making your content feel forced.

Where to Place Related Terms

There's no magic formula, but there are some smart habits.

First, your title and H1 should focus on your primary keyword. That hasn't changed, but your subheadings (H2s and H3s) are a natural place to bring in related concepts. If you're writing about "home office setup," subheadings like "Choosing the Right Desk Chair" and "Monitor Placement for Ergonomics" naturally introduce related terms without any forced insertion.

Your body copy should read naturally. If you find yourself writing a sentence just to include a related term, that's a red flag. The term should add meaning, not just fill space.

• Use related terms in subheadings where they fit the structure naturally
• Mention them in the introduction and conclusion when relevant
• Include them in image alt text and captions
• Work them into bullet lists and summaries
• Use them in meta descriptions to reinforce page context

How Many is Too Many?

This is one of the most common questions, and the honest answer is: there's no number.

Think about it differently. A 2,000-word article on "email marketing" should naturally mention terms like "open rate," "subject line," "segmentation," "click-through rate," and "unsubscribe." You're not counting how many times to use them. You're writing thoroughly and they appear because the topic demands them.

If you're reviewing a draft and a tool is flagging ten "missing LSI keywords," don't panic. Ask yourself: would a reader benefit from this information? If yes, work it in naturally. If no, skip it. Coverage for coverage's sake adds bloat, not value.

Writing for People First, Search Engines Second

This sounds like a cliché, but it's the most practical advice available. When you write content that genuinely helps a reader, you'll naturally use the vocabulary of that topic. You won't need to reverse-engineer which "LSI keywords" to insert.

Real talk: the best SEO content in 2026 is written by people who actually know their subject, or it's written with tools that replicate that depth. Either way, topical expertise is what shows up in the words you choose.

If you're struggling to write naturally about a topic, that's a signal to do more research, not to find more keyword variations to paste in.

If you're creating content at scale, manually researching related terms for every article isn't realistic. That's where a tool like Semly Pro earns its place in your workflow.

How Semly Pro Handles Semantic SEO

Semly Pro generates long-form SEO articles that naturally incorporate semantically relevant vocabulary. You don't need to feed it a list of "LSI keywords" and tell it where to put them. The AI understands topic context and writes accordingly.

What sets it apart from generic AI writing tools is the SEO layer built on top of the writing. The AI visibility score shows you how well your content is likely to appear in AI-generated search results, not just traditional blue links. That matters in 2026 when AI Overviews and LLM-driven answers are a real source of traffic.

Other features that directly support semantic SEO include:

• AI competitor detection to see what terms and topics rivals are covering
• AI citation tracking to monitor where your content gets referenced
• LLMs. txt generation to help AI systems understand your site's content structure
• Custom brand voice to ensure consistency across all generated articles
• CMS publishing to 12 platforms so content goes live without manual copying

Semly Pro runs on three tiers. The Pro plan at €139/mo covers 40 long-form SEO articles per month and 25 AI tracking prompts. Business Pro at €229/mo scales to 100 articles and 50 AI tracking prompts across 3 projects. The Managed SEO plan at €469/mo puts a dedicated SEO strategist in charge of the whole operation.

Semly Pro vs. Other SEO Tools: Feature Comparison

The table shows where Semly Pro stands apart, particularly on AI visibility features that other tools haven't caught up with yet. If your goal is SEO content that performs in both traditional search and AI-driven results in 2026, it's worth starting a free trial.

Even people who understand the concept well often make these errors. Let's go through them.

Treating LSI Keywords Like Old-School Keyword Stuffing

This is the most common one. Someone finds a list of "LSI keywords" from a tool, then inserts them mechanically throughout the article. The result reads like a spam page from 2009.

Search engines in 2026 are sophisticated enough to recognize this, and more importantly, readers notice immediately. Forced keyword insertion breaks the flow of good writing and makes the whole piece feel less trustworthy.

The fix is simple. Write first, optimize second. Get your ideas down in natural prose, then check whether you've naturally covered the important related terms. Fill gaps where they exist, and leave the rest alone.

Ignoring Search Intent While Chasing Terms

You can include every semantically related term in the book and still rank poorly if your content doesn't match what the searcher actually wanted to find.

Search intent is the "why" behind a query. Someone searching "LSI keywords" might want a beginner explanation. Someone searching "LSI keywords tool" wants a product recommendation. Someone searching "are LSI keywords a myth" wants a nuanced take on the debate.

If you're writing for the wrong intent, no amount of related-term optimization will save you. Always confirm intent first by looking at what's already ranking. Then make sure your content matches that intent before you think about which terms to include.

Skipping Topic Clusters Entirely

A single article using great related terms is good. A whole topic cluster built around a pillar page is much better.

Topic clusters mean you have a main pillar article covering the core topic broadly, with supporting articles going deep on specific subtopics, all linking back to the pillar. This structure signals to Google that your site genuinely covers a topic in depth, not just one page.

LSI keywords and related terms play a big role here. Each supporting article will naturally introduce its own vocabulary, and together they build a picture of your authority on the topic. If you're only thinking about keyword terms at the article level, you're missing the bigger opportunity.

Not every related term you find deserves a spot in your content. You need to be selective. Here's a repeatable process that works.

Step 1: Start with Your Core Topic

Before you look at any tool, write down everything you'd expect a thorough article about your topic to cover. Just brainstorm. No filtering yet.

If your topic is "plant-based diet," you'd probably jot down things like protein sources, B12 deficiency, meal planning, grocery costs, ethical reasons, environmental impact, and health benefits. That list is already full of naturally relevant terms.

Now compare that list to what your SEO tools surface. You'll often find significant overlap. The gaps between your brainstorm and the tool results are what you should pay attention to. Those are concepts you might not have thought of but that real searchers care about.

Step 2: Group Terms by Intent

Once you've got a solid list of related terms, group them. Some terms are informational. Others are commercial. Some are very specific and might belong in a separate, more focused article rather than this one.

For example, if you're writing a beginner's guide to LSI keywords, terms like "TF-IDF" or "vector space model" might be too technical for this audience and could get their own article. Including them in a beginner piece would make it harder to read and wouldn't serve the reader's actual needs.

Grouping by intent helps you decide what goes in the current article and what becomes the seed for a future one.

Step 3: Prioritize Coverage, Not Count

The question isn't "how many LSI keywords did I include?" The question is "does this article fully cover the topic for someone who genuinely wants to learn about it?"

A good test: read your article as if you know nothing about the topic. Did it answer all your obvious follow-up questions? Did it use the vocabulary someone familiar with the topic would expect to see? Did it cover the main angles without getting lost in tangents?

If yes, you're done. If something's missing, add it. That's the whole process.

Honestly, it's less complicated than most SEO tools make it seem. The fundamentals haven't changed even as the technology has gotten more advanced. Write well, cover your topic completely, and use the natural vocabulary of your subject matter.

That's what LSI keyword strategy looks like when it's done right.

What are LSI keywords in SEO?

LSI keywords, short for Latent Semantic Indexing keywords, are words and phrases that are conceptually related to your main keyword. in practice, they're the natural vocabulary of a topic. Including them helps search engines understand what your content is really about and confirms that you're covering a subject thoroughly rather than just repeating a single phrase.

Does Google actually use LSI?

No. Google has publicly stated it doesn't use Latent Semantic Indexing. What it does use is far more advanced, including neural language models like BERT and MUM that understand context and meaning at a much deeper level. The practical implication is the same: Google rewards content that covers a topic with appropriate depth and vocabulary. The label is outdated, but the strategy behind it is still valid.

Are LSI keywords the same as long-tail keywords?

Not quite. Long-tail keywords are longer, more specific search queries, usually with lower search volume. LSI keywords are related terms that share conceptual ground with your main keyword. There's sometimes overlap, but they're different concepts. A long-tail keyword might be a related term, but most related terms aren't long-tail keywords in the traditional sense.

How do I find LSI keywords for free?

Start with Google itself. Autocomplete suggestions, People Also Ask boxes, and related searches at the bottom of the results page are all free and highly reliable. AnswerThePublic and AlsoAsked are also free tools that surface question-based related terms effectively. For existing pages, Google Search Console shows the queries your pages already rank for, which often reveals related terms you hadn't planned for.

How many LSI keywords should I use per article?

There's no magic number. The goal is coverage, not count. Write thoroughly about your topic and the relevant vocabulary will appear naturally. Then review your draft to identify any obvious gaps. If important related concepts are missing, add them where they fit naturally. Don't aim for a specific number; aim for complete topic coverage.

Can using too many LSI keywords hurt my rankings?

It's unlikely that related terms themselves cause a penalty. What can hurt you is the quality problems that come with forcing too many terms in. Unnatural phrasing, bloated paragraphs, and content that reads like a keyword checklist rather than a genuine article all reduce quality signals. The terms themselves aren't the problem. Poor writing is.

What's the difference between LSI keywords and semantic SEO?

Semantic SEO is the broader discipline of optimizing content for meaning and context rather than just exact keyword matches. LSI keywords are one way people talk about implementing semantic SEO, even if the technical label is imprecise. Semantic SEO also includes things like topic clusters, structured data, entity optimization, and search intent matching. LSI keyword research is one tactical component of a larger semantic strategy.

Do LSI keywords help with featured snippets?

They can, indirectly. Featured snippets tend to come from content that clearly and directly answers a question within a well-structured, topically thorough article. When you cover related terms and subtopics well, you're also building the kind of depth that Google tends to pull snippets from. It's not a direct cause-and-effect, but thorough semantic coverage and snippet selection often go together.

How does Semly Pro help with LSI keywords and semantic SEO?

Semly Pro generates long-form SEO articles that naturally incorporate semantically relevant vocabulary without requiring you to manually research and insert related terms. The AI understands topic context and writes accordingly. Features like the AI visibility score, AI competitor detection, and LLMs. txt generation go further than traditional keyword tools by helping your content perform in AI-driven search results, not just standard blue links. Plans start at €139/mo for solo marketers.

Are LSI keywords still relevant in 2026?

The concept is. The label is outdated, but the practice of writing content that uses the full vocabulary of your topic remains essential. Google's ability to understand language has only gotten stronger, which means thin content that targets one keyword and ignores related concepts performs worse than ever. in 2026, semantic depth isn't optional; it's expected. The sites that rank consistently are the ones treating topics completely, not keyword-hunting in isolation.