LSI Keywords, Semantic Keywords, and Topic Clusters for AI Search
“LSI keywords” is an SEO term for words related to your main keyword, named after latent semantic indexing, an information retrieval method from the late 1980s. Google has said plainly that it does not use LSI keywords. What does matter is semantic coverage: answering the related questions, naming the right entities, and organizing content into topic clusters that both search engines and AI systems can understand.
That shift from keywords to topics is central to how we approach answer engine optimization. This guide explains the myth, the reality, and a practical way to plan content around topics.
What does LSI mean?
Latent semantic indexing (also called latent semantic analysis) was described by Scott Deerwester, Susan Dumais, and colleagues in a 1990 paper, building on work patented in the late 1980s. It used a mathematical technique (singular value decomposition) to find relationships between terms in a fixed set of documents, so a search for “car” could also surface documents about “automobile.”
It was designed for small, static document collections. The web is neither small nor static.
Does Google use LSI keywords?
No. In July 2019 Google’s John Mueller wrote on Twitter (now X) that “there’s no such thing as LSI keywords” and that anyone saying otherwise is mistaken. He has repeated the point since.
So why does the term persist? Because the advice attached to it is partly right. Pages that cover a topic thoroughly naturally use related terms, and those pages often rank well. The cause is thorough coverage, not a list of “LSI keywords” sprinkled into the copy.
Google’s 2026 guide to generative AI features makes the same point from another angle: its AI systems “can understand synonyms and general meanings,” so you do not need to capture every variation of how someone might search. It also warns that creating many pages for query variations, mainly to manipulate rankings or AI answers, can violate its scaled content abuse policy.
What to use instead: semantic keywords and entities
Semantic keywords are terms and questions that are meaningfully related to your topic, because a good answer would naturally include them. For “how to choose a CRM,” that includes pipeline stages, contact management, integrations, pricing per seat, and data migration.
Entities are distinct, identifiable things: people, organizations, products, places, concepts. Google’s Knowledge Graph and large language models both work with entities. Naming the right ones, accurately and consistently, helps systems understand what a page is about and how your brand relates to a topic.
The practical difference:
| LSI keyword approach | Semantic and entity approach |
|---|---|
| Generate a list of related words with a tool | Map the questions a reader needs answered |
| Insert terms at a target density | Cover subtopics because the answer requires them |
| Focus on one page and one keyword | Plan a cluster of pages around a topic |
| Measure by term count | Measure by rankings across many queries, citations, and engagement |
Why semantic coverage matters for AI search
Two mechanisms make topic coverage more important now than it was five years ago.
Query fan-out. Google says AI Overviews and AI Mode may issue several related searches across subtopics before writing an answer. A page that covers the important subtopics has more chances to be retrieved. Ahrefs’ March 2026 update found only about 38% of AI Overview citations ranked in the top 10 for the original query, which it links to fan-out across related queries.
Language models learn associations. Models predict likely text based on what appears together in their training data. If your brand consistently appears alongside the entities and concepts in your category, on your site and on others’, models are more likely to make that connection. This is one reason brand mentions on third-party pages matter, as covered in brand mentions for SEO.
What is a content pillar?
A content pillar (or pillar page) is a comprehensive page covering a broad topic, which links to more detailed pages on each subtopic. The detailed pages are the cluster. Together they form a topic cluster.
Example for a payroll software company:
- Pillar: Payroll for small businesses: the complete guide
- Cluster pages: How to run payroll yourself; payroll tax deadlines by state; contractor vs employee payments; how to choose payroll software; payroll software pricing explained; common payroll mistakes
Each cluster page links back to the pillar and to closely related siblings. The pillar links out to each cluster page.
(The term “content pillars” is also used in social media marketing to mean the three to five recurring themes a brand posts about. Same idea, different channel.)
How to build a topic cluster
1. Start with the topic, not the keyword
Choose a topic where you have real expertise and commercial relevance. Write one sentence on what a reader should be able to do after reading the whole cluster.
2. Map the questions
Gather questions from:
- Google’s “People also ask” and related searches
- Search Console queries already sending impressions
- Sales calls and support tickets
- AI engines: ask ChatGPT, Gemini, or Perplexity what someone would need to know about the topic, then check the claims yourself
- Keyword tools, grouped by meaning rather than exact wording
3. Group by intent
Use semantic keyword clustering: put queries that a single page could answer well in the same group. If two queries would get different answers, they need different pages. If the answers are the same, one page is enough.
4. Decide pillar vs cluster pages
The broadest, highest-level question becomes the pillar. Distinct sub-questions with enough depth become cluster pages. Small sub-questions become sections or FAQs within an existing page.
5. Write answer-first
Open each page with a direct, specific answer in the first 40 to 60 words, then add depth, examples, data, and sources. The GEO research published at KDD 2024 found that adding citations, quotations, and statistics improved visibility in generative answers.
6. Link with purpose
Use descriptive internal anchors that tell readers what they will get. Every cluster page links to the pillar near the top. Avoid using the same exact anchor repeatedly.
7. Name entities clearly
Use full names for products, organizations, standards, and people on first mention. Keep your own brand name and description consistent everywhere. Where you use structured data, make sure it matches the visible page; Google says no special schema is needed for its AI features.
8. Update the cluster, not just pages
Review the whole cluster at least twice a year. Merge thin pages, update facts, and add new sub-questions as they appear.
Common mistakes
- Keyword-variation pages. Separate pages for “best CRM for startups” and “top CRM for startups” split authority and risk scaled content issues.
- Tool-generated term lists. Adding words because a tool suggested them, without adding meaning.
- Clusters without depth. Ten 400-word pages that repeat the pillar.
- Orphan pages. Cluster pages nobody links to.
- Ignoring off-site. A strong cluster on your site is half the job; AI engines also read what others say about you. See how to rank in AI Overviews.
Frequently asked questions
What is an LSI keyword in SEO?
A term SEOs use for words related to a main keyword. It borrows the name of an old retrieval method, latent semantic indexing, which Google says it does not use.
Are LSI keyword generators useful?
They can suggest related terms and questions, which may help you spot gaps. Treat the output as research prompts, not a checklist to insert.
What are semantic keywords in SEO?
Words, phrases, and questions closely related in meaning to your topic, which a complete answer would naturally cover.
What is the difference between keywords and topics in SEO?
A keyword is a specific search phrase. A topic is the broader subject that many keywords belong to. Modern search systems rank pages on how well they cover the topic and satisfy the intent, not on exact phrase matches.
How many pages should a topic cluster have?
As many as the topic genuinely needs. Some clusters work with five pages, others need twenty. Each page should answer a distinct question.
