What Are LSI Keywords?

LSI stands for Latent Semantic Indexing. In SEO usage, LSI keywords are described as terms and phrases that are semantically related to your primary keyword. They are framed not as strict synonyms but as words that frequently appear alongside a given topic, on the theory that they help search engines understand the context and meaning of your content.

Two facts need to be stated plainly up front. First, the term is a misapplication. Latent Semantic Indexing is a real information retrieval technique introduced by Deerwester, Dumais, Furnas, Landauer, and Harshman in their 1990 paper "Indexing by Latent Semantic Analysis," which applies singular value decomposition to a term-by-document matrix to model relationships between words and documents. It was built for small, static document collections, not for the live web. Second, Google does not use it. Google Search Advocate John Mueller addressed this directly in 2019, stating "There's no such thing as LSI keywords" and elsewhere "we have no concept of LSI keywords, so that's something you can completely ignore." So "LSI keywords" is best understood as an industry shorthand for semantic relevance, not as a real Google ranking input.

For example, if your primary keyword is "apple," related terms might include "iPhone," "macOS," and "Tim Cook" for the technology context, or "orchard," "fruit," and "pie" for the food context. These related terms genuinely help readers and modern language models disambiguate which version of "apple" your content is about. The mechanism Google actually uses to do that is documented below, and it is not LSI.

Why LSI Keywords Matter

Search engines have moved far beyond simple keyword matching. Google uses sophisticated natural language processing to understand what a page is actually about, not just which exact phrases it contains.

They improve topical relevance. When your content naturally includes related terms, it signals depth and authority on the subject. A page about "marathon training" that also mentions "running schedule," "hydration," "carb loading," and "race day preparation" demonstrates comprehensive coverage.

They reduce keyword stuffing risk. Instead of repeating your primary keyword over and over, you can use semantically related terms to reinforce your topic. This creates content that reads naturally while still sending strong relevance signals.

They help you rank for more queries. Pages that cover a topic thoroughly tend to rank for dozens or hundreds of related search queries, not just the primary keyword. LSI keywords expand your ranking footprint.

They improve user experience. Content that covers related concepts is genuinely more useful to readers. It answers follow-up questions before they are asked and provides a more complete resource.

How LSI Keywords Work

The original LSI technique comes from information retrieval research published in 1990. It uses singular value decomposition, a mathematical analysis that identifies latent relationships between terms across a collection of documents. Google does not run this technique. What Google actually documents are three AI systems for understanding meaning. RankBrain, per Google Search Central, "helps us understand how words are related to concepts" so it can return relevant content even when a page lacks the exact query words. Neural matching lets Google "understand representations of concepts in queries and pages and match them to one another." BERT, "Bidirectional Encoder Representations from Transformers," lets Google "understand how combinations of words express different meanings and intent." The practical takeaway is unchanged from the folk advice: covering a topic with its naturally related terms helps these systems read your page. The label "LSI" is just wrong for the underlying technology.

Here is how to find the related terms people loosely call LSI keywords, and use them well:

1. Google autocomplete. Start typing your keyword in Google and note the suggested completions.
2. Related searches. Scroll to the bottom of any Google results page and review the "Related searches" section.
3. People Also Ask. The PAA box shows questions related to your keyword, each containing potential LSI terms.
4. Keyword research tools. Most tools have a "related terms" or "semantic keywords" feature.
5. Competitor content. Read the top-ranking pages for your keyword and note which supporting terms they use.

When writing content, weave these terms in naturally. They should appear in headings, body text, image alt attributes, and meta descriptions where relevant.

Best Practices

Write for humans first. LSI keywords should appear naturally within well-written content. If a sentence feels forced because you jammed in a related term, rewrite it.

Cover the topic comprehensively. The best way to include LSI keywords is to thoroughly cover your subject. When you explain all aspects of a topic, the related terms appear organically.

Use them in headings. Subheadings that incorporate semantically related terms help both search engines and readers understand your content structure.

Do not treat them as a checklist. There is no magic number of LSI keywords to include. Focus on writing the best possible content on your topic. The related terms will follow naturally.

Combine with primary keyword research. LSI keywords complement your main keywords. They are not a replacement for proper keyword research but an enhancement that adds depth and context.

Common Mistakes

Over-optimizing for LSI terms. Some SEOs compile massive lists of related terms and try to force every single one into their content. This creates awkward, unnatural writing that hurts both user experience and rankings.

Confusing LSI keywords with synonyms. LSI keywords are contextually related, not just different words for the same thing. "Running shoes" is a synonym for "athletic footwear." An LSI keyword for "running shoes" might be "pronation" or "arch support."

Using outdated LSI tools. Some dedicated "LSI keyword generators" are unreliable and produce irrelevant results. Stick to Google's own suggestions, reputable SEO tools, and manual analysis of top-ranking content.

Ignoring search intent. Related terms only help if they align with what the searcher actually wants. Including LSI keywords about "buying running shoes" in a purely informational article about running technique creates a mismatch.

Thinking LSI is a ranking factor. It is not. John Mueller stated flatly in 2019 that "there's no such thing as LSI keywords." The principle of topical relevance and semantic understanding is very much part of how modern search works, delivered through RankBrain, neural matching, and BERT rather than through latent semantic indexing. Focus on the concept, not the discredited technical label.

Conclusion

LSI keywords represent the broader concept of semantic relevance in SEO, even though the LSI label itself is technically wrong and Google does not use latent semantic indexing. By including naturally related terms in your content, you help Google's actual language systems understand your topic more accurately and demonstrate comprehensive coverage. The key is to write thoroughly about your subject and let the related terminology emerge naturally, rather than forcing terms into your content from a list. When done right, semantically rich content ranks better, reads better, and serves your audience more effectively.

In Practice

Suppose you are writing a guide and your primary keyword is "espresso machine." The folk "LSI" approach says to sprinkle in related terms. The grounded approach is to cover the topic completely, which causes those terms to appear on their own.

Before, a thin paragraph stuffed with the exact phrase:

The best espresso machine is the espresso machine that makes espresso. When choosing an espresso machine, pick the espresso machine that fits your budget for an espresso machine.

After, the same topic written for a reader, with related concepts woven in naturally:

Choosing a home espresso machine comes down to a few mechanics. Pump pressure is measured in bars, with nine bars being the espresso standard. A single boiler heats one thing at a time, while a dual boiler lets you pull a shot and steam milk for a latte at once. A built-in grinder, a portafilter, and a steam wand round out the setup, and PID temperature control keeps extraction consistent.

The second version never repeats the head term mechanically, yet it ranks for a far wider set of queries because it actually answers what buyers ask. Terms like "bars of pressure," "PID," "dual boiler," "portafilter," and "steam wand" are what people mean by LSI keywords. You did not add them from a list. You added them by explaining the subject. That is the same signal RankBrain, neural matching, and BERT are built to read.

Related Terms

• What Is Semantic Search? covers how Google interprets meaning and intent rather than literal strings, which is the real mechanism behind the LSI myth.
• What Is Search Intent? explains why matching the reason behind a query matters more than matching its words.
• What Is Keyword Stuffing? describes the over-optimization trap that misusing related terms can lead to.
• What Is Keyword Research? walks through finding the primary terms that related terminology supports.
• What Is Topical Authority? shows how comprehensive topic coverage, the genuine value behind LSI advice, builds ranking strength.

Sources

• Google Search Central, A Guide to Google Search Ranking Systems (RankBrain, neural matching, BERT descriptions), checked on 2026-05-30: https://developers.google.com/search/docs/appearance/ranking-systems-guide
• Google, "Understanding searches better than ever before" (BERT launch), checked on 2026-05-30: https://blog.google/products-and-platforms/products/search/search-language-understanding-bert/
• Search Engine Journal, "Latent Semantic Indexing (LSI): Is It A Google Ranking Factor?" (John Mueller quotes), checked on 2026-05-30: https://www.searchenginejournal.com/ranking-factors/latent-semantic-indexing/
• ERIC record for Deerwester et al. (1990), "Indexing by Latent Semantic Analysis," Journal of the American Society for Information Science 41(6), 391 to 407, checked on 2026-05-30: https://eric.ed.gov/?id=EJ415308
• Deerwester, Dumais, Furnas, Landauer, Harshman (1990), original paper PDF, checked on 2026-05-30: http://wordvec.colorado.edu/papers/Deerwester_1990.pdf