"Add LSI keywords" is one of the most repeated pieces of SEO advice — and one of the most misunderstood. The term comes from a decades-old information-retrieval technique that Google does not use. Yet the advice behind it, covering a topic with the related words and concepts a knowledgeable writer would naturally use, is genuinely sound.
This guide untangles the myth from the useful idea: where the term LSI came from, how modern search engines really understand content, how many related terms a page needs, how to find them, and how to build a long-term semantic SEO strategy. For applying related keywords to titles, headings, links and specific page types, see using related keywords on the page.
What LSI Keywords Actually Are and Where the Term Comes From
Latent semantic indexing (LSI) is a mathematical technique developed in the late 1980s for retrieving documents from fixed collections. It analyses which words tend to appear together across documents, so a search for "car" could also surface documents about "automobiles". It was designed for small, static document sets — not a web of billions of constantly changing pages.
SEO tools later borrowed the name, and "LSI keywords" came to mean related words and phrases — synonyms, associated terms and sub-topics. Google representatives have stated plainly that Google does not use LSI keywords. The label is technically wrong; the underlying idea of semantically related terms is what actually matters.
Common Misconceptions About LSI Keywords, Cleared Up
- "Google uses LSI." It does not. Modern search uses far more advanced language models and entity understanding.
- "You need a list of LSI keywords to rank." You need to cover the topic well. Related terms appear naturally when you do.
- "More related keywords means better rankings." Stuffing in terms from a tool reads badly and can look manipulative.
- "Synonyms are interchangeable." Not always — "cheap flights" and "budget airlines" can have different intent.
- "LSI tools show what Google wants." They show co-occurring terms from top-ranking pages, which is a useful hint, not a requirement.
How Modern Search Engines Understand Content
Why LSI Keywords Matter for How Search Engines Understand Content Today
The reason related terms still matter is that search engines evaluate whether a page genuinely covers a topic. An article about baking sourdough that never mentions starter, hydration, proofing or crust is probably not very thorough. Related terms are evidence of depth — a side effect of real coverage that search systems, and readers, recognise.
How LSI Keywords Connect to Modern Language Understanding Algorithms
Google's systems have moved from matching words to understanding meaning. Milestones include the Hummingbird update (2013), which focused on the meaning of whole queries; RankBrain (2015), which helped interpret unfamiliar queries; and BERT (2019), which models how words relate to each other in context — understanding that "to" in "flights from Brazil to the USA" changes everything. Later language models extend this further. These systems understand synonyms, context and intent directly, which is why exact keyword repetition matters far less than it once did.
How Search Engines Use Semantic Analysis to Understand Content
Beyond individual words, search engines identify entities — people, places, products, organisations, concepts — and the relationships between them, drawing on resources like Google's Knowledge Graph. A page about "Python" is understood as the programming language or the snake based on surrounding entities: "pandas", "functions" and "IDE" versus "venom", "habitat" and "constrictor". Semantic analysis is how a page ranks for queries it never mentions word for word.
Related Keywords vs Exact Match and Density
LSI Keywords vs Exact-Match Keywords: What's the Real Difference?
An exact-match keyword is the specific phrase you target ("sourdough bread recipe"). Related keywords are the surrounding vocabulary of the topic ("starter", "levain", "bulk fermentation", "Dutch oven"). The primary keyword tells search engines and readers what the page is about; related terms show that the page covers it properly. You need both — the main phrase in prominent places like the title and opening, and related terms throughout.
How Related Keywords and Keyword Density Actually Relate to Each Other
Keyword density — the percentage of words that are your target keyword — is an outdated metric. There is no ideal density, and repeating a phrase to hit a number makes writing worse. Related terms make density largely irrelevant: a page that uses varied, natural vocabulary signals relevance without repetition. Write for clarity; if a sentence sounds unnatural when read aloud, rewrite it.
How Many Related Keywords Should Actually Show Up on a Page?
There is no target number. A short answer to a narrow question may need only a handful of related terms; a comprehensive guide will naturally use dozens. Rather than counting, check coverage: does the page answer the main questions, define important concepts, and address the sub-topics competing pages cover? If yes, the related vocabulary is almost certainly there.
Using LSI Keywords to Avoid Accidental Keyword Stuffing
Related terms are the cure for repetition. Instead of writing "best hiking boots" eight times, write about waterproofing, ankle support, break-in time, trail runners and fit. The page stays clearly about hiking boots while reading naturally. If you notice the same phrase appearing in nearly every paragraph, replace some instances with related terms or pronouns.
Finding Related Keywords
How to Actually Find LSI Keywords for a Given Topic
Practical sources:
- Search results: autocomplete, People Also Ask, "related searches" at the bottom of the page, and bolded terms in snippets.
- Top-ranking pages: headings and recurring terms across the best results.
- Your audience: forums, reviews, support questions and community discussions.
- Subject knowledge: list what an expert would expect any good explanation to mention.
- Tools: related-keyword tools that extract co-occurring terms from ranking pages.
Making Sense of What an LSI Keyword Tool's Data Actually Tells You
Related-keyword tools typically analyse top-ranking pages for a query and list terms that frequently appear. That is useful for spotting sub-topics you missed. But it reflects correlation, not causation: some terms appear because of site navigation or boilerplate, and some are irrelevant to your angle. Use the list as a checklist of ideas to consider, include terms where they genuinely help the reader, and ignore the rest.
Why Depth and Related Terms Go Together
LSI Keywords and Content Depth: Why They Go Together
Depth produces related vocabulary automatically. When you explain causes, steps, examples, variations, mistakes and comparisons, you use the full vocabulary of the subject. That is why rewriting a thin page to be genuinely comprehensive usually improves rankings for many more queries than adding a list of keywords ever could.
LSI Keywords and E-E-A-T: How Semantic Depth Supports Trust
Google's quality guidelines describe E-E-A-T: experience, expertise, authoritativeness and trustworthiness. Expert writing uses precise vocabulary, explains nuance and covers edge cases — content written by someone who knows the subject naturally includes the terms and distinctions a novice would miss. Related terms are not a substitute for expertise, but genuine expertise shows up partly through them, alongside author credentials, sources and first-hand experience.
LSI Keywords and Topical Relevance: The Bigger Picture
Relevance is judged across a site, not just a page. A site that covers a subject thoroughly across many interlinked pages builds topical authority — search engines can see it is a genuine resource on that topic. Related keywords across a cluster of pages, connected with descriptive internal links, reinforce that picture.
How Related Keywords Affect Reader Experience, Not Just Search Rankings
Readers notice repetition and thin coverage. Varied, precise vocabulary makes content easier to read and more satisfying, and answering related questions keeps readers on the page instead of returning to the search results. Content that serves readers well tends to perform well in search — the two goals rarely conflict.
Building a Semantic SEO Strategy
Related Keywords as Part of a Broader Semantic SEO Strategy
Semantic SEO means organising content around topics and entities rather than individual keywords. In practice:
- Define the core topics your site should be known for.
- Map the sub-topics, questions and entities within each.
- Build pillar pages for broad topics and supporting pages for specific questions.
- Interlink them with descriptive anchor text.
- Use structured data where it helps search engines understand entities (products, organisations, articles, recipes).
Building a Long-Term Semantic SEO Strategy Around Related Keywords
Semantic coverage is built over time:
- Audit existing content for topic gaps and thin pages.
- Plan clusters that fill the gaps.
- Publish supporting pages and strengthen pillars.
- Refresh older pages as topics evolve, adding new sub-topics and terminology.
- Measure the breadth of queries each page ranks for in Search Console — growing query counts are a sign your semantic coverage is improving.
The Bottom Line
"LSI keywords" is a misnomer, but the idea it points to is real: search engines understand meaning and topics, and they reward pages that cover a subject properly. Forget density and keyword lists; cover the topic thoroughly, use the natural vocabulary of an expert, and organise your site into well-linked topic clusters.
Next step: Find related terms for any topic with the free LSI Tool, then use them as a coverage checklist, not a stuffing list.