9 min read · On-Page SEO · Last updated July 2026
Quick answer: “LSI keywords” as used in SEO is technically a misnomer — Latent Semantic Indexing is a 1980s algorithm Google doesn’t use. What matters in 2026 are semantically related terms: words and concepts that expert coverage of a topic naturally includes. The practical advice is similar, but the mechanism is entirely different.
Introduction
If you’ve been adding “LSI keywords” to your content based on a plugin or tool recommendation, you’ve been doing the right thing for the wrong reason.
Latent Semantic Indexing was developed at Bell Labs in the late 1980s as a document retrieval technique. Google has confirmed, on multiple occasions, that it does not use LSI in its ranking algorithm. The term became an SEO buzzword in the 2010s and stuck around despite being technically wrong.
Here’s what’s actually happening when you add “related keywords” to your content: you’re improving topical coverage, and that matters enormously. The mechanism isn’t LSI — it’s Google’s modern NLP models understanding that comprehensive topic coverage is a signal of genuine expertise.
What you’ll learn:
– What LSI actually is and why Google doesn’t use it
– What semantically related terms are and how they work
– How to find terms that actually improve topic coverage
– Why the practical advice is still useful, even though the name is wrong
Table of Contents
- What LSI Actually Is
- Why Google Doesn’t Use LSI
- What Actually Matters: Semantic Co-occurrence
- How to Find Genuinely Related Terms
- Using Related Terms Without Stuffing
- LSI Keyword Tools: Are They Useful?
- Related Term Finder Widget
- FAQ
What LSI Actually Is
Latent Semantic Indexing (LSI) is a mathematical technique that uses singular value decomposition to identify patterns in the relationships between terms and documents. Published in 1988 by Deerwester et al., it was designed to improve document retrieval by finding conceptually related documents even when they didn’t share exact keyword matches.
The core idea: documents about similar topics use similar vocabularies. An article about “cars” will contain terms like “engine,” “driver,” “fuel,” “highway.” An article about “automobiles” (a synonym) will contain the same terms. LSI could identify these as related documents even without explicit keyword matching.
This was genuinely useful in 1988. Google’s 2006-era algorithms might have used concepts related to it. Current Google uses large language models that are orders of magnitude more sophisticated. The comparison is like suggesting Google uses punch cards for computation.
Why Google Doesn’t Use LSI
Google’s John Mueller has addressed this directly. In a 2019 Reddit AMA, he stated: “There’s no such thing as LSI keywords — this is a myth that some SEOs have been pushing for years.”
The reason: LSI requires analyzing your content against a static corpus of documents and decomposing it mathematically. Google’s actual systems are dynamic, real-time, and use transformer-based language models (BERT, and more recently, Gemini) that handle semantic understanding fundamentally differently.
What Google does do:
– Understand queries and documents using transformer models trained on billions of text samples
– Extract entities and their relationships from content
– Evaluate whether a document covers a topic comprehensively
– Assess the quality and factual grounding of content
None of these are LSI. But they do mean that including topically relevant terms in your content helps — just through a completely different mechanism.
What Actually Matters: Semantic Co-occurrence
What you actually need to think about is semantic co-occurrence: terms that frequently appear together with your topic in high-quality text across the web.
Google’s language models have been trained on enormous text corpora and have learned that:
– Articles about “email marketing” consistently include terms like “open rate,” “subject line,” “segmentation,” “deliverability”
– Articles about “project management” consistently mention “milestones,” “dependencies,” “Agile,” “Gantt charts”
– Articles about “content strategy” consistently include “editorial calendar,” “audience research,” “content brief,” “distribution”
When your article on email marketing is missing all of these terms, Google’s models notice the unusual absence — because every other high-quality email marketing article includes them. The absence signals either shallow coverage or off-topic content.
The practical advice that “LSI keyword” recommendations give — include related terms in your content — is correct. The terminology and mechanism described are just wrong.
How to Find Genuinely Related Terms
Since LSI keyword tools are giving you semantically related terms through a different mechanism than advertised, here are better ways to find terms that actually improve coverage:
Read the top-ranking pages. The most direct method. Open the top 5 results for your keyword. What subtopics, tools, concepts, and terms do they consistently cover that your draft doesn’t? That’s your gap list.
Google’s “People Also Ask” and “Related Searches.” These surfaces show what else searchers ask about your topic — and what terms Google associates with your query. Include content that addresses these adjacent questions.
Wikipedia’s article on your topic. Skim the article and its linked topics. The concepts that Wikipedia links to from your topic’s article are legitimate related concepts.
Content optimization tools (used correctly). Clearscope, Surfer SEO, and MarketMuse do analyze top-ranking content and surface frequently-occurring terms. Use these outputs as a coverage checklist — not a stuffing list. If a term appears, ask whether it represents a concept you should genuinely cover. If yes, add content about that concept. If it’s truly irrelevant, skip it.
Using Related Terms Without Stuffing
The goal is topic coverage, not term frequency. There’s a right and wrong way to include related terms:
Right: Dedicate a section to the related concept. If “deliverability” is a term you should include for an email marketing article, write a 100-200 word section explaining deliverability and its importance. The term appears naturally throughout that section.
Wrong: Adding the term in a sentence where it doesn’t belong. “Our email marketing guide covers segmentation, subject lines, and deliverability.” → This mentions the term without covering the concept.
Right: Using the term naturally within a discussion of the broader topic. “When you segment your list, deliverability improves because you’re sending more relevant content to smaller, more engaged groups.”
Wrong: Generating a list of related terms and inserting each one once into otherwise unrelated sentences.
LSI Keyword Tools: Are They Useful?
LSI Graph, KeywordShitter with LSI mode, and similar tools surface terms that frequently co-occur with your target keyword in top-ranking content. Despite the inaccurate name, this output can be useful as a starting point for identifying coverage gaps.
Use them as:
– A quick survey of which related concepts top content covers
– A comparison against your draft to identify gaps
– Input for building a more complete content brief
Don’t use them as:
– A list to insert term-by-term into existing content
– A replacement for actually reading top-ranking pages
– A guarantee of ranking improvement from inclusion
The best “LSI keyword” research tool is reading the content that already ranks and asking: what does this cover that I don’t?
FAQ
Q: If Google doesn’t use LSI, why do my rankings improve when I add “LSI keywords”?
A: Because the practical effect — adding related concepts and semantically relevant terms — genuinely improves content quality and topical coverage. Your rankings improved because you created more comprehensive content, not because you applied LSI. Same outcome, different mechanism.
Q: Should I still use LSI keyword tools?
A: They’re useful as a rough guide to what related terms appear in top content, despite the inaccurate name. Use the output as a coverage checklist, not a prescription. Combine it with reading the actual top-ranking pages for your keyword.
Q: Is there a target number of “LSI keywords” to include?
A: No. This is a made-up metric with no basis in how Google actually works. Cover the topic comprehensively — address all major subtopics, mention relevant entities, and answer sub-questions. The number of specific terms is a byproduct of good coverage, not a target.
Q: What’s the difference between LSI keywords and secondary keywords?
A: Secondary keywords are variations of your primary keyword that you also want to rank for (“email marketing software,” “email marketing tools,” “email marketing platforms”). LSI keywords (properly understood) are related concepts that signal topical authority. Both are worth including — for different reasons.
Q: Do LSI keywords help with voice search?
A: Related terms help because voice queries tend to be more conversational and topically specific. If your content covers the full range of related concepts, it’s more likely to match the natural language variations that voice search uses. But the mechanism is semantic topic coverage, not LSI.
Conclusion
The terminology is wrong, but the core advice isn’t: comprehensive content that covers the full semantic neighborhood of your topic outperforms narrow content that repeats a single phrase. Call them “related terms,” “semantic terms,” or “topic coverage terms” — not LSI keywords. And find them by reading what already ranks, not by running a keyword through a tool with an outdated name.
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Written by the Ignited Nepal SEO team. ignitednepal.com