If you’ve spent any time researching SEO strategies, you’ve likely encountered the term “LSI keywords.” These supposedly magical terms have been promoted as essential components of content optimization, with claims that they can boost your rankings and help search engines better understand your content.
But what exactly are LSI keywords? Do they actually exist in the way they’re commonly described? And most importantly—do they really matter for your SEO strategy?
This article will dive deep into the truth about LSI keywords, separate fact from fiction, and provide practical guidance on how to approach related terms and semantic search optimization in a way that aligns with how modern search engines actually work.
What Are LSI Keywords?
LSI stands for Latent Semantic Indexing, which is a mathematical technique developed in the late 1980s to improve information retrieval systems. The term “LSI keywords” has become widely used in SEO circles to describe words and phrases that are semantically related to your main target keyword.
For example, if your primary keyword is “apple,” supposed “LSI keywords” might include terms like:
- For the fruit context: orchard, pie, cider, varieties, nutrition
- For the technology company: iPhone, Mac, iOS, Tim Cook, App Store
According to the common understanding in many SEO blogs, these related terms help search engines determine the context and subject matter of your content, thus improving your rankings for relevant searches.
However, there’s a fundamental problem with this definition and approach—it’s based on a misunderstanding of both LSI technology and how modern search engines actually work.
The Origin of LSI in Search
Latent Semantic Indexing is a real mathematical technique developed in the late 1980s. It uses singular value decomposition, a method from linear algebra, to identify patterns in the relationships between terms and concepts in unstructured text.
LSI was designed to address challenges in traditional keyword-based information retrieval, particularly:
- Synonymy: Different words can express the same concept (e.g., “car” and “automobile”)
- Polysemy: The same word can have multiple meanings (e.g., “bank” as a financial institution or the side of a river)
By analyzing patterns of word co-occurrence across documents, LSI could create a “semantic space” where related terms were positioned closer together, allowing search systems to find relevant documents even when they didn’t contain the exact query terms.
The Common Misconception About LSI Keywords
Here’s where the confusion begins: while LSI is a real technology, “LSI keywords” as commonly described in SEO blogs don’t actually exist as a concept used by Google or other major search engines.
The misconception stems from several sources:
- Outdated information: LSI was indeed an important advancement in information retrieval when it was developed. However, it was designed for much smaller document collections than the modern web.
- Misunderstanding of technology: Many articles about “LSI keywords” demonstrate a fundamental misunderstanding of what LSI actually is and how it works.
- Confusion with broader semantic search: As search engines evolved to better understand context and meaning (semantic search), some SEO practitioners incorrectly attributed these advancements to LSI.
Google representatives, including John Mueller and Gary Illyes, have explicitly stated that Google doesn’t use LSI. In fact, Gary Illyes once tweeted: “There’s no such thing as LSI keywords—anyone who’s telling you otherwise is mistaken, sorry.”
What Google Actually Uses Instead of LSI
Instead of LSI, modern search engines like Google use much more sophisticated approaches to understand content meaning and relevance:
- Machine learning models: Advanced algorithms that can understand language nuances and context far beyond what LSI could achieve.
- Neural networks: Deep learning systems like BERT (Bidirectional Encoder Representations from Transformers) and more recently LaMDA that comprehend language more like humans do.
- Knowledge Graph: A vast network of entities (people, places, things, concepts) and their relationships that helps Google understand the world’s information.
- Natural Language Processing (NLP): Sophisticated techniques for analyzing and understanding human language patterns.
These technologies allow search engines to grasp subtle contextual differences and relationships between terms that go far beyond simple word co-occurrence patterns that LSI would identify.
Why the Term “LSI Keywords” Persists
Despite being technically incorrect, the term “LSI keywords” continues to appear in SEO content for several reasons:
- Industry inertia: Once terminology becomes established in an industry, it can be difficult to correct, even when it’s based on misunderstanding.
- Simplified explanation: The concept of “semantically related terms” is easier to explain by using the shorthand “LSI keywords,” even if it’s technically incorrect.
- Content recycling: Many SEO blogs reference older content without verifying the technical accuracy of the concepts.
- Tool marketing: Numerous SEO tools market features for finding “LSI keywords,” perpetuating the misconception to sell their services.
Do LSI Keywords Matter for SEO?
So, if LSI keywords don’t technically exist in modern search algorithms, should you ignore the concept entirely?
The answer is nuanced. While “LSI keywords” as commonly described don’t exist, the underlying principle—that including semantically related terms can help search engines understand your content—is valid.
Here’s what actually matters:
- Comprehensive coverage: Content that thoroughly covers a topic naturally includes related terms, synonyms, and contextual phrases.
- Natural language: Writing in a natural, information-rich way that addresses user intent will naturally incorporate semantically related terms.
- Topical relevance: Building content that demonstrates expertise on a subject by covering related concepts and questions.
What doesn’t matter:
- Forced keyword density: Artificially stuffing content with supposed “LSI keywords” won’t improve rankings and may read as unnatural.
- Lists of automatically generated “LSI keywords”: Blindly incorporating terms from tools without considering their relevance to your content.
- Focusing on specific “LSI keyword” placement techniques: Modern search algorithms are too sophisticated to be manipulated by such simplistic approaches.
Better Alternatives to “LSI Keywords”
Instead of focusing on the outdated concept of LSI keywords, here are more accurate ways to think about related terms in your content:
Semantic Keywords: Semantic keywords are terms that share meaning or context with your primary keyword. They help build a comprehensive semantic profile of your content. Unlike the misnamed “LSI keywords,” semantic keywords reflect how modern search engines actually analyze content.
Related Keywords: Related keywords are simply terms that tend to appear in similar contexts or discussions as your main topic. They help expand your content’s reach by addressing different aspects of the subject.
Entity-Based SEO: Entity-based SEO focuses on the people, places, things, and concepts related to your topic. By properly addressing entities and their relationships, you create content that aligns with how knowledge graphs organize information.
Moving Beyond LSI Keywords
The concept of “LSI keywords” as commonly described in SEO literature is based on outdated and misunderstood technology. Google and other modern search engines use far more sophisticated approaches to understand content meaning and relevance.
However, the underlying principle—that comprehensive content naturally includes related terms and concepts—remains valid. By focusing on creating thorough, valuable content that genuinely addresses user needs, you’ll naturally incorporate the semantic relationships that modern search algorithms recognize.
Rather than chasing “LSI keywords,” focus on:
- Understanding your audience: What questions do they have? What information do they need?
- Covering topics comprehensively: Address the subject from multiple angles and provide complete information.
- Writing naturally for humans: Create content that reads well and provides value, not just content optimized for an outdated understanding of search algorithms.
- Staying current with search technology: Follow reliable sources about how search engines actually work rather than recycling outdated concepts.
By taking this more sophisticated approach, you’ll create content that performs well in modern search engines while also genuinely serving your audience’s needs—the ultimate win-win for effective SEO strategy.