What Are LSI Keywords? How to Use Them in SEO

In this article, you'll learn what LSI keywords actually are, why Google has dismissed them as a myth, and what you should use instead. You'll also discover practical methods for finding semantically related keywords that help search engines understand your content, along with how to apply these same principles to improve your visibility in AI search engines like ChatGPT, Claude, and Perplexity.

What Are LSI Keywords?

LSI keywords have been marketed as words or phrases semantically related to your primary keyword. If you're writing about coffee brewing methods, supposed LSI keywords might include "grind size," "water temperature," "extraction time," and "French press."

The theory goes like this: by sprinkling these related terms throughout your content, you signal to Google that your page comprehensively covers the topic. This supposedly helps search engines understand context and rewards you with higher rankings.

The problem? This entire framework is built on a misunderstanding of what Latent Semantic Indexing actually is and whether Google uses it at all.

Before we debunk the myth, let's understand where it came from.

What Is Latent Semantic Indexing?

Latent Semantic Indexing (LSI) is an information retrieval technique developed by researchers at Bell Labs in the late 1980s. The method analyzes relationships between words and documents to identify patterns in how terms appear together.

Here's how LSI works in simple terms: the technology scans large collections of text and builds a mathematical model of which words frequently appear alongside each other. If "car," "engine," and "transmission" often show up in the same documents, LSI recognizes these terms as related.

The original patent, filed by Susan Dumais in 1989, applied this technique to a small, static collection of documents. The use case was helping users find relevant papers in a fixed database—not crawling and indexing the constantly changing web.

This distinction matters. LSI was designed for closed document sets that don't change. The internet adds millions of new pages daily. The computational approach that worked for a library of research papers doesn't scale to billions of web pages updated in real time.

Yet somewhere in the mid-2000s, SEO practitioners began connecting LSI to Google's algorithm. The idea spread that Google used Latent Semantic Indexing to understand content, and that adding "LSI keywords" would boost rankings.

The SEO community ran with this theory for years. Tools emerged promising to generate LSI keywords. Blog posts outlined LSI keyword strategies. The term became standard vocabulary in SEO discussions.

There was just one problem: Google never actually used LSI.

Google's Stance on LSI Keywords

John Mueller, Google's Search Advocate, addressed the LSI keyword myth directly on Twitter:

"There's no such thing as LSI keywords—anyone who's telling you otherwise is mistaken, sorry." — John Mueller, July 30, 2019

Google's own spokesperson explicitly stated that LSI keywords don't exist in the way the SEO community describes them.

Bill Slawski, one of the most respected SEO researchers who spent years analyzing Google patents, reinforced this point:

"After looking through most of Google's patents and papers, there are no papers that describe the effectiveness of LSI Keywords. There are papers on Semantic Topic Models, which have nothing to do with LSI Keywords."

Slawski went even further in his assessment:

"LSI keywords do not use LSI, and are not keywords."

The evidence is clear. Despite years of SEO content promoting LSI keyword strategies, Google's official position and independent patent research both confirm the same conclusion: LSI keywords as commonly understood don't influence Google's ranking algorithm.

Why LSI Keywords Aren't Important for SEO

Understanding why LSI keywords don't matter requires looking at both the technical limitations and how Google actually processes content.

The Technology Predates the Web

Susan Dumais filed the LSI patent in 1989. The World Wide Web launched in 1991. Google was founded in 1998.

The original LSI use case involved analyzing a fixed set of academic documents to help researchers find related papers. The patent explicitly describes working with a small, unchanging corpus of text.

Applying 1980s information retrieval technology to a dynamic, ever-expanding internet would be like using a card catalog system to organize Amazon's entire product inventory. The scale and complexity are fundamentally different.

Patent Timing Made Adoption Impractical

US patents typically receive 20 years of protection. The LSI patent would have been protected until 2009, meaning Google couldn't have used the technology in its core algorithm during its formative years from 1998 to 2009.

By the time patent protection expired, Google had already developed far more sophisticated natural language processing capabilities. Why adopt 20-year-old technology when you've built something better?

Google Uses More Advanced Methods

A 2017 Google patent revealed the search engine uses Word Vector technology to understand content relationships. Word2Vec and similar neural network approaches process language in ways that dramatically outperform LSI.

These modern techniques understand that "king" is to "queen" as "man" is to "woman." They recognize that "New York" and "Big Apple" refer to the same place. They process nuance and context in ways LSI never could.

Google's BERT update in 2019 and subsequent language model improvements pushed content understanding even further. The search engine now processes the meaning of entire sentences and paragraphs, not just individual keyword relationships.

LSI Tools Encourage Poor Practices

Most LSI keyword generators simply produce lists of related terms and encourage you to add them to your content. This approach often leads to keyword stuffing—cramming terms into your writing regardless of whether they fit naturally.

Google has explicitly penalized keyword stuffing for years. If your "LSI keyword strategy" involves inserting terms from a tool-generated list without regard for readability, you risk triggering spam filters rather than improving rankings.

The irony is that following LSI keyword advice often produces exactly the kind of unnatural, over-optimized content that Google's algorithms are designed to detect and demote.

What You Should Use Instead: Semantically Related Keywords

The concept behind LSI keywords isn't entirely wrong—just the terminology and execution. Search engines do benefit from content that thoroughly covers a topic using relevant terminology. The distinction is between LSI keywords (a myth) and semantically related keywords (a real ranking factor).

Semantically related keywords are terms that share conceptual connections with your primary topic. They help search engines understand what your content covers and whether it satisfies user intent.

Here's the difference in practice:

LSI keyword approach: Find a list of "LSI keywords" and insert them into your content a specific number of times.

Semantic keyword approach: Write comprehensive content that naturally includes the terminology your audience uses when discussing this topic.

When you write about mortgage refinancing, you'll naturally mention interest rates, closing costs, loan terms, and equity. These aren't LSI keywords you need to artificially inject—they're the vocabulary of the topic itself.

Google's phrase-based indexing patents show the company invests heavily in understanding how terms relate to each other in natural language. Context vector patents reveal efforts to determine intent when search terms have multiple meanings.

For example, if someone searches "python," Google uses surrounding context and user behavior signals to determine whether they're looking for information about the programming language or the snake. Content that includes "code," "programming," and "libraries" signals one meaning. Content mentioning "habitat," "species," and "venom" signals another.

This is semantic understanding, not LSI. The distinction matters because it changes how you approach content creation.

Rather than stuffing terms from a keyword tool, you should write content that comprehensively addresses your topic in the language your audience actually uses.

How to Find and Use Semantically Related Keywords

Now that we've established what actually works, here are practical methods for finding semantically related keywords and incorporating them into your content.

Method 1: Analyze Top-Ranking Pages

The most reliable way to understand what Google considers relevant is to examine what's already ranking. Top-performing pages have proven they satisfy both search intent and Google's content quality standards.

Here's how to do this manually:

Step 1: Search for your target keyword in Google.

Step 2: Open the top 5-7 organic results (skip any that are clearly irrelevant, such as glossary entries or branded results that don't match your content type).

Step 3: Read each page and note recurring terms and phrases. Pay attention to:

• Words that appear in multiple H2 and H3 headings

• Technical terminology used across several articles

• Questions these pages answer

• Specific examples and case studies mentioned

Step 4: Create a list of the most frequently appearing relevant terms.

Step 5: Review your own content and identify which terms you've covered and which are missing.

Bill Slawski described his own process this way:

"I search for the query term that I want to rank a page for, and I usually look at the top 10 ranking pages in Google for that query term that match the meaning of the query term that I am trying to rank for. I look for complete phrases that appear on those pages and co-occur a number of times."

This manual approach works but takes time. SEO tools like Surfer, Clearscope, and Frase automate this analysis by scanning top-ranking pages and extracting commonly used terms.

If you're using Surfer's Content Editor:

1. Enter your target keyword

2. Select your country and device preferences

3. Click "Create Content Editor"

4. Review the keyword suggestions panel showing terms extracted from top-ranking pages

The tool will display recommended terms and suggested usage frequency based on what's working for pages that currently rank.

Target a content score above 75 to ensure you've covered the semantic territory that Google associates with your topic.

Method 2: Use Google's SERP Features

Google's search results page itself reveals what the search engine considers relevant to any query. Several SERP features expose semantic relationships directly.

Related Searches

Scroll to the bottom of any Google search results page to find the "Related searches" section. These suggestions show terms and questions Google associates with your query.

For an article about email marketing automation, related searches might include "best email automation tools," "email drip campaign examples," and "marketing automation vs email marketing." Each of these represents semantic territory you might address in comprehensive content.

People Also Ask

The People Also Ask (PAA) box reveals questions Google users frequently have about your topic. Each question represents a potential section or subtopic for your content.

Clicking any PAA question expands it and generates additional related questions. You can expand several questions to build a comprehensive list of semantically related subtopics.

These questions often make excellent H2 or H3 headings. If users are asking "How long should an email drip campaign be?" and you're writing about email automation, answering that question directly strengthens your semantic coverage.

Knowledge Panels

For queries about entities (people, places, companies, concepts), Google often displays a Knowledge Panel with structured information. The attributes shown reveal what Google considers important facts about that entity.

If you're writing about a specific topic or entity, ensure your content includes the key facts that appear in the Knowledge Panel. This demonstrates comprehensive coverage of the semantic space.

Featured Snippets

When Google displays a featured snippet for your target query, that content represents what the algorithm considers the best direct answer. Analyze the snippet's structure, terminology, and approach.

Your content should provide an equally direct answer while expanding with additional depth and examples.

Method 3: Audit Existing Content for Gaps

If you've already published content that isn't ranking as expected, a semantic gap analysis can reveal missing terms that would strengthen your coverage.

The process involves comparing your content against top-ranking competitors to identify terminology you haven't included.

Using Surfer's Audit tool:

1. Enter your page URL and target keyword

2. Select country and device preferences

3. Click "Create Audit"

1. In the "Select competitors" section, choose relevant ranking pages to compare against. Exclude any branded results, e-commerce pages, or content types that don't match yours.

1. Navigate to the "Terms to Use" report

1. Sort by the Action column to see the most critical recommendations

The report shows terms you should add, terms to reduce (possible over-optimization), and your current usage compared to competitors.

Focus on terms where competitors consistently use terminology you haven't included. These represent semantic gaps that may be hurting your rankings.

Method 4: Study Topic Clusters and Related Entities

Google's understanding of content goes beyond individual keywords to encompass entities and their relationships. An entity is any distinct thing that can be defined: a person, place, concept, or object.

Entity-based SEO involves ensuring your content clearly establishes relationships between relevant entities in your topic space.

For example, if you're writing about project management software, relevant entities might include:

• Tools: Asana, Monday.com, Trello, Jira

• Concepts: Gantt charts, Kanban boards, sprint planning

• Use cases: Team collaboration, task assignment, deadline tracking

• Related categories: Resource management, time tracking, workflow automation

Comprehensive content mentions the key entities in your topic space and explains their relationships. This signals to Google that your content provides authoritative coverage rather than surface-level treatment.

Wikipedia's approach demonstrates entity-focused content well. Articles consistently link to related topics, creating clear entity relationships that search engines can understand.

You don't need to link every term, but your content should naturally reference the entities associated with your topic.

How to Apply Semantic Keywords for AI Search Visibility

Everything we've covered so far applies to traditional Google SEO. But search is evolving beyond the ten blue links. ChatGPT, Claude, Perplexity, and other AI engines now answer questions directly, and their approach to content differs from Google's in important ways.

The good news: semantic keyword principles transfer to AI search. The same comprehensive, well-structured content that ranks in Google tends to get cited by AI engines.

The nuance: AI engines have different citation patterns and source preferences. Understanding these differences helps you optimize for both channels simultaneously.

AI Engines Interpret Semantic Relationships Differently

AI language models don't crawl and index the web the same way Google does. They're trained on massive text datasets and learn language patterns through that training. When they retrieve information to answer queries, they may access the web through real-time search or rely on training data, depending on the engine and configuration.

This means AI engines process semantic relationships through the lens of language model training, not just document analysis. They understand synonyms, related concepts, and topical connections through patterns learned from billions of text examples.

For content creators, this reinforces the value of natural, comprehensive writing over keyword manipulation. AI models are particularly good at detecting when content sounds unnatural or over-optimized.

Source Preferences Vary by Engine

Research from Analyze AI's study of 83,670 citations across ChatGPT, Claude, and Perplexity revealed significant differences in how each engine sources information.

Claude heavily favors blog content, citing it for 43.8% of mentions. ChatGPT and Perplexity prefer product pages and official documentation (60.1% and 54.3% respectively).

This has practical implications. If you're optimizing for Claude visibility specifically, investing in comprehensive blog content pays dividends. For ChatGPT visibility, ensuring your product pages and documentation are thorough and well-structured matters more.

Third-party sources dominate across all engines, accounting for about 83% of citations. Only 17% of citations point to brand websites directly. This means earning mentions and coverage from review sites, industry publications, and authoritative third-party sources significantly impacts your AI visibility.

Track Which Semantic Topics Drive AI Visibility

Traditional keyword ranking tools don't capture AI search visibility. You might rank well in Google for a term but never appear when users ask AI engines related questions.

Analyze AI lets you track visibility across ChatGPT, Claude, and Perplexity for specific prompts related to your semantic keyword clusters.

Here's how to use this for semantic keyword research:

Step 1: Identify your core semantic topics and the prompts users ask AI engines about them.

Step 2: Set up prompt tracking in Analyze AI for these queries.

Step 3: Monitor which semantic topics generate visibility and which show gaps.

The Prompt Suggestion feature helps identify additional prompts in your topic space that you might not have considered.

Step 4: Analyze which sources AI engines cite when discussing your topics.

If competitors consistently get cited for semantic topics where you have content, examine what their content includes that yours doesn't. The citation sources reveal exactly which URLs AI engines trust for specific topic areas.

Monitor Competitor Semantic Coverage

The Competitor Overview in Analyze AI shows how often competitors appear alongside your brand in AI responses.

When you track competitors, you can see which prompts they win and you don't. The Opportunities view specifically highlights prompts where competitors are mentioned but your brand is absent.

Each opportunity represents a semantic topic where you might need stronger content. If Salesforce appears in AI responses for "best CRM for small business" and you don't, that's a content gap to address.

The gap isn't just about whether you have a page on the topic. It's about whether your content provides the depth, structure, and authority that AI engines trust enough to cite.

How to Measure the Impact of Your Semantic Keyword Strategy

Creating content optimized for semantic keywords is only half the equation. You need measurement systems to understand what's working and where to invest further.

Traditional SEO Measurement

For Google rankings, standard SEO tools track your progress:

Keyword rankings: Monitor position changes for your target keyword and related terms. If you've added semantic coverage, you should see improvements for related queries, not just your primary keyword.

Organic traffic: Google Analytics and Search Console show traffic trends. Filter by landing page to see whether your optimized content is attracting more visitors.

Search Console impressions: The Performance report reveals which queries your content appears for. Expanding semantic coverage should increase impressions for related queries over time.

SERP features: Track whether you're winning featured snippets, People Also Ask inclusions, or other SERP features for semantic keyword variations.

AI Search Measurement

AI engine visibility requires different tools since traditional rank trackers don't capture this channel.

Analyze AI's AI Traffic Analytics connects to your Google Analytics and attributes sessions from AI engines.

This shows:

• Total sessions from AI search in the last 30 days

• AI traffic as a percentage of total site traffic

• Trend over time showing whether AI-driven visits are growing

The page-level breakdown reveals which landing pages receive AI traffic.

If certain pages consistently attract AI traffic, analyze what makes them work. Is it comprehensive semantic coverage? Strong structure? Specific content types?

The Analytics by Engine view shows which AI platforms drive traffic to your site.

This helps prioritize optimization efforts. If Perplexity drives most of your AI traffic, understanding Perplexity's source preferences (which favor product documentation and feature pages) guides content strategy.

Connect Visibility to Business Outcomes

Visibility metrics only matter if they translate to business results. Track the full funnel:

Traffic: Are AI-optimized pages attracting visitors?

Engagement: Do visitors from AI engines engage with your content (time on page, pages per session, scroll depth)?

Conversions: Are AI-sourced visitors converting at rates comparable to other channels?

Analyze AI's case study with Kylian showed AI search traffic converting at 5%—significantly above typical blog benchmarks of 1-2%. Certain pages like "Best Online English Courses" converted at 8.3%.

This level of attribution lets you calculate the actual ROI of semantic content optimization for AI search, not just track vanity visibility metrics.

Applying Semantic Keywords: A Practical Framework

Let's consolidate everything into an actionable process you can follow for any new piece of content.

Before Writing

1. Research top-ranking pages for your target keyword. Note recurring terminology, questions answered, and content structure.
   
   

2. Check SERP features for semantic keyword ideas from People Also Ask, Related Searches, and Knowledge Panels.
   
   

3. Identify key entities in your topic space that comprehensive content should reference.
   
   

4. Review AI engine citations for your topic using Analyze AI's citation analytics to understand what sources AI engines trust.
   
   

1. Create a content brief that includes your primary keyword, semantic variations, questions to answer, and entities to mention.

While Writing

1. Write naturally without forcing keywords. If your content comprehensively covers the topic, semantic terms will appear organically.
   
   

2. Structure content with clear headings that address specific questions and subtopics. H2s and H3s should reflect the semantic areas users care about.
   
   

3. Include specific examples, data, and details. Vague content lacks the substance that both Google and AI engines reward.
   
   

4. Reference relevant entities and explain their relationships to your topic.
   
   

5. Answer questions directly before expanding with additional context. AI engines especially favor content that provides clear, direct answers.
   
   

After Publishing

1. Submit to Google Search Console for faster indexing.
   
   

2. Monitor traditional rankings for your primary keyword and semantic variations.
   
   

3. Track AI visibility using Analyze AI to see whether AI engines cite your content.
   
   

1. Identify citation gaps by checking which sources AI engines cite when discussing your topic.
   
   

2. Iterate based on data. If certain semantic areas generate more AI visibility or traffic, create additional content expanding those topics.

Key Takeaways

LSI keywords as commonly discussed in SEO circles don't exist. Google has explicitly stated it doesn't use Latent Semantic Indexing, and the technology itself predates the web and wouldn't scale to modern search.

What does matter is semantic relevance. Writing comprehensive content that naturally covers your topic using the terminology your audience uses signals quality and relevance to search engines.

The principles that improve Google rankings also apply to AI search visibility. ChatGPT, Claude, and Perplexity all favor content that thoroughly addresses topics with clear structure and authoritative information.

The difference is measurement. Traditional SEO tools don't capture AI visibility. Understanding how AI engines cite sources and which content types each engine prefers requires tools built specifically for this channel.

By combining semantic keyword optimization with AI visibility tracking, you build content that performs across both traditional and AI search—maximizing your reach without duplicating effort.

The brands winning today don't treat SEO and AI search as competing channels. They recognize AI search as an evolution of organic visibility and optimize for both simultaneously. Your semantic keyword strategy should do the same.