What Is Latent Semantic Indexing?
Latent Semantic Indexing (LSI) is a mathematical information retrieval technique introduced in the late 1980s that identifies patterns between terms and concepts in a collection of documents. Rather than treating each word in isolation, LSI uses a statistical method called Singular Value Decomposition (SVD) to uncover the hidden (latent) relationships between words and the topics they represent.
In the context of search engine optimisation, LSI means that search engines do not simply look for the exact phrase you typed.
When someone in Johannesburg searches for "home loan," Google understands that a page discussing "bond," "property finance," "repayment," and "interest rate" is about the same topic, even if it never contains the phrase "home loan" verbatim.
The engine identifies co-occurring terms that tend to appear together across millions of documents and uses that knowledge to assess topical relevance.
From a practical SEO standpoint, LSI reinforces the importance of writing comprehensive, natural content rather than repeating the same keyword phrase throughout a page. A page about solar panels in South Africa should naturally mention load shedding, inverters, kilowatts, installation, Eskom, and rebates.
These related terms signal to search engines that the content genuinely covers the topic, not that it has been written to manipulate a single keyword.
It is worth noting that the specific term "LSI keywords" is somewhat misleading in modern SEO. Google has moved far beyond the original LSI algorithm and now uses neural-network-based language models such as BERT and MUM to understand language.
The underlying concept, however, remains valid: writing topically rich content with naturally related vocabulary improves rankings far more than keyword stuffing ever did.
Latent Semantic Indexing In Practice
The scenario below is an illustrative example, not a Juicy Designs client result. It shows the kind of approach that semantic content work typically involves, so treat it as indicative rather than measured.
Consider a South African accounting firm wanting to rank for "small business tax." A page written with LSI principles in mind would naturally include terms such as SARS, provisional tax, income tax return, CIPC registration, VAT threshold, tax deductions, and financial year-end.
None of these terms would need to be forced in; they are simply part of any thorough discussion of small business tax in South Africa.
A content team applying LSI thinking would research competitor pages that rank well and identify the related terms those pages cover. They would then ensure their own content addresses the same sub-topics, not by copying, but by covering the subject comprehensively.
Tools like Google's People Also Ask results, autocomplete suggestions, and related searches all surface the vocabulary that real South African users associate with a given topic.
The result would be content that reads naturally for human visitors while also demonstrating topical authority to search algorithms. This approach pairs well with topic cluster strategies, where a pillar page on a broad topic is supported by cluster pages on related subtopics, collectively building a body of content that search engines recognise as genuinely authoritative in its field.
What latent semantic indexing actually is
Latent semantic indexing (LSI) is a decades-old information-retrieval technique that analyses relationships between terms and the documents they appear in to identify patterns of meaning, helping a system understand that certain words tend to occur together in documents about a topic. It is a genuine mathematical technique from information science. However, its relevance to modern SEO is widely misunderstood and overstated. The term became popular in SEO circles, giving rise to the idea of "LSI keywords", supposedly related terms you should include to rank, but Google has stated it does not use LSI as SEO marketers describe, and the technique, developed for small, static document collections, is not how modern search engines with their vast, dynamic indexes and advanced language models actually understand content. So while LSI is a real concept in its original field, the SEO notion built on it, of specific "LSI keywords" to sprinkle into content, does not reflect how Google works, which is important to understand before acting on advice framed around it.
The LSI keywords myth and what actually matters
The popular SEO advice to add "LSI keywords" rests on a misunderstanding, and chasing them is largely wasted effort. Google does not use latent semantic indexing in the way the advice implies, and there is no list of magic related terms that, sprinkled in, will lift rankings. What is true, and what the LSI idea gestures at clumsily, is that modern search engines understand topics through meaning, context and relationships between concepts, using far more advanced techniques than LSI, such as the language-understanding systems behind BERT and semantic search. The genuine takeaway is not to hunt for "LSI keywords" but to cover a topic thoroughly and naturally: when you write comprehensively and clearly about a subject, the related terms and concepts appear on their own, because they genuinely belong, and this natural, thorough coverage is what signals relevance and depth to search engines. So the productive response to the LSI idea is to ignore the myth of special keywords and instead focus on genuinely comprehensive, well-written content on a topic, which is what modern semantic understanding actually rewards.
FAQ
Do I need to include LSI keywords in my content?
Writing naturally tends to include related terms organically. Focus on covering your topic thoroughly rather than inserting specific LSI keywords. Google's algorithms have moved well beyond simple keyword matching and reward comprehensive, well-written content.
How does LSI relate to semantic SEO?
LSI is an earlier precursor to modern semantic SEO. Today's search engines use more advanced techniques including neural networks and embeddings, but the core principle remains the same: content that covers a topic thoroughly with related terms ranks better than content stuffed with one keyword.
Do you need to include LSI keywords in content?
No. "LSI keywords" is a misunderstanding: Google has said it does not use latent semantic indexing as SEO advice implies, and there is no list of magic related terms that lifts rankings. What matters is covering a topic thoroughly and naturally, so related terms appear because they genuinely belong, which is what modern semantic understanding rewards.