LSI Keywords: Do They Still Matter for SEO in 2025?

If you’ve ever wondered whether LSI keywords still influence rankings, you’re not alone. The term appears in countless SEO guides, yet modern search engines no longer rely on LSI at all. What they do care about is meaning, how clearly your content explains a topic, how well it reflects user intent, and whether it includes the entities and contextual signals readers expect. This guide breaks down the real story behind LSI, what actually affects on-page relevance in 2025, and how to optimize your content for both search engines and AI-driven platforms.

For over a decade, “LSI keywords” has been one of the most persistent terms in SEO. The phrase appears in training courses, keyword tools, and audit templates, usually presented as a technique that improves search visibility by adding “related keywords” to a page. Many content creators still treat LSI terms as a ranking formula, believing that Google rewards pages that include long lists of synonyms or associated words. Yet the conversation has shifted, especially in the last few years, as SEO has moved beyond keyword matching to semantic understanding.

The debate now centers on a simple question: does Google still use anything like LSI, and if not, what actually improves relevance in a world of AI-first search engines? The rise of contextual ranking systems like BERT and MUM, and the rapid growth of generative platforms such as ChatGPT and SGE, has changed how content must signal meaning. In this environment, the idea of LSI feels outdated, yet the underlying motivation; helping search engines understand context remains incredibly important. It’s just that the methods have evolved.

This article breaks down that evolution through a practical lens. Rather than simply declaring “LSI is dead” or sticking to rigid keyword rules, we explore how semantic relationships truly work, how modern engines interpret content, and how you can write in a way that satisfies both human readers and the systems that summarize, rank, or generate answers. By the end, you’ll see why the term “LSI keywords” lingers in marketing circles and how to replace it with an approach grounded in clarity, meaning, and entity-based optimization.

What LSI Keywords Originally Meant

To understand the modern conversation, it helps to revisit the original meaning of LSI; not how the SEO industry used the word, but how researchers defined it decades ago. Latent Semantic Indexing was an early technique from the late 1980s used to analyze large sets of documents. The model attempted to uncover hidden relationships between words by placing them in a mathematical space where distances corresponded to relevance.

In academic terms, LSI used singular value decomposition (SVD) to reduce the dimensionality of term-document matrices. By mapping words and documents into a shared vector space, the model could identify patterns in how terms co-occurred. If two words frequently appeared in similar contexts, LSI treated them as semantically related even if they were not direct synonyms. At the time, this was an important advancement because it helped simple search systems find relevant content despite vocabulary variations.

However, this method belonged to an era when computing power was limited and natural language processing was still primitive. What mattered then was overcoming basic keyword matching. LSI provided a way to approximate meaning, but it was never designed for the scale, complexity, or real-world nuance of modern web content. Most importantly, it was never part of Google’s core ranking systems. Google’s founders even noted that such models were not scalable for the web. Yet the terminology resurfaced years later through marketers who wanted a shorthand for “related keywords.”

Understanding this gap between the academic definition and the SEO reinterpretation is crucial. It shows how easily technical terms can drift away from their original purpose and turn into broad concepts that survive long after the underlying methods are obsolete.

How the Term “LSI Keywords” Became Misunderstood

As search engines evolved, SEO tools and content marketers needed new ways to talk about context and relevance. Many of the early on-page optimization systems focused heavily on single keywords, density calculations, and repeated phrases. When this approach stopped working effectively, vendors began promoting “LSI keywords” as a more advanced tactic.

The problem was that what they called LSI keywords were not actually produced through latent semantic indexing. In most cases, the lists were created using simple term co-occurrence analysis, scraping top-ranking pages, or pulling synonym suggestions from a thesaurus. Yet calling them “LSI keywords” gave the concept an academic flavor that suggested technical sophistication. As the term spread, many SEOs began believing Google itself used LSI in ranking, a misconception Google engineers like John Mueller repeatedly denied. Independent Patent researchers on SEO like Bill Slawski also denied role of LSI in SEO.

Part of the confusion came from the fact that these tools often produced useful output. A list of semantically related phrases can indeed help expand topical coverage, which often leads to better rankings. But the mechanism behind that improvement has nothing to do with LSI. It comes from improving the page’s completeness, answering user needs more clearly, and aligning with modern ranking systems that evaluate topical depth rather than mechanical keyword matches.

Once the term became widespread, it was hard to dislodge. Even today, new marketers encounter guides recommending LSI techniques, unaware that the phrase has no direct connection to the systems that power Google, Bing, or AI search engines. This creates a tension between two truths. First, LSI keywords as described by SEO tools are not part of Google’s ranking algorithms. Second, related terms, co-occurring concepts, and contextual language do influence how engines interpret your content. The trick is understanding the difference.

How Modern Search Engines Interpret Meaning

Google’s shift from keyword-driven ranking to semantic understanding reshaped the entire concept of relevance. The introduction of RankBrain, followed by BERT and later MUM, pushed search away from matching terms and toward interpreting user intent, entities, and contextual relationships. Rather than relying on models from the 1980s, these systems use neural networks capable of capturing linguistic nuance at a much deeper level.

Modern search engines do not care about synonyms in the old-fashioned sense. They care about meaning. When someone searches for “symptoms of low iron,” Google understands the query relates to iron deficiency anemia, fatigue, dietary deficiencies, and potential medical treatments. It does not need a web page to include a predefined list of LSI terms. Instead, it evaluates how comprehensively the page addresses the topic, how well the content aligns with the searcher’s intent, and how clearly it references entities and concepts associated with the topic.

Semantic search models also analyze relationships between entities, such as people, places, products, and ideas. This is where knowledge graphs play a large role, connecting concepts into structured networks. Because of this, pages that describe a topic clearly, include supporting concepts naturally, and communicate factual relationships almost always perform better. This is why long-form, well-organized content tends to outrank short sections stuffed with keywords, not because length is a ranking factor, but because depth helps systems infer meaning.

In practice, this means a page no longer ranks because it uses “low iron levels symptoms” alongside “signs of anemia” or “fatigue causes.” It ranks because it clearly explains those concepts in a useful, cohesive way that matches what users expect. Search engines care about the clarity of communication, not mechanical phrase insertion. This shift is one of the reasons the LSI keyword myth feels outdated, even though the desire for contextual richness still matters.

Do LSI Keywords Still Influence Rankings Today?

The short answer is no; not in the way many SEO guides suggest. Google does not use latent semantic indexing, nor does it reward content for including sets of “LSI terms.” That said, the long answer is more nuanced.

Search engines evaluate whether a page addresses a topic comprehensively. They look for signals that the content clearly expresses the subject matter, anticipates user intent, and provides the necessary details a reader might expect. Pages that do this naturally tend to contain related phrases, but these come from thoughtful writing rather than keyword lists.

So when people observe that “adding related terms helps rankings,” their conclusion is right but their reasoning is off. Adding meaningful details improves rankings because it strengthens topical depth and aligns with semantic models, not because the terms are part of an LSI formula.

This distinction matters because chasing LSI lists can lead to unnatural writing or scattered topics. The more effective approach is to think about the topic holistically. What questions do readers have? What related concepts appear in the top-performing content? What entities does Google associate with the subject? These kinds of insights guide the use of contextual vocabulary that actually supports ranking.
When improving topical relevance, even basic things like optimizing your title tags for relevance help search engines understand the primary focus of your page.

Entity-Based Optimization vs LSI: What Actually Matters

While LSI continues to circulate in SEO conversations, the real driver of relevance today is entity-based optimization. Entities are identifiable concepts, such as people, products, conditions, or locations that search engines can understand independently of the specific words used to describe them. Google’s Knowledge Graph and similar systems in Bing and AI search models map the relationships between these entities, allowing engines to interpret content at a deeper level.

When you optimize for entities, you focus on defining the key concepts within your topic and clarifying their relationships. A page about vitamin D deficiency, for example, will naturally mention sunlight exposure, supplements, bone health, blood tests, and medical guidance. These are not LSI terms; they are entities and attributes that belong to the topic. Including them helps the engine understand what your page is about in the same way a human reader would.

This explains why content that thoroughly covers a topic tends to perform well even if it doesn’t rely heavily on specific keywords. The presence of relevant entities signals completeness, expertise, and contextual understanding. These are qualities modern ranking systems reward.

Entity-based optimization also fits neatly into AI search. When generative engines produce summaries, they rely on associated concepts, factual relationships, and structured context. This means well-organized content with clear entity references is more likely to appear in AI-generated answers.

Seeing this shift highlights why LSI feels outdated. It represents a world of keyword proximity, not the world of semantic relationships and entities that now drives search across Google, Bing, and emerging AI platforms.

How AI Search (ChatGPT, SGE, Perplexity) Handles Semantics

As AI-driven engines grow in influence, understanding how they interpret meaning becomes as important as traditional organic rankings. Unlike earlier search systems that relied heavily on web indexes and keyword relevance, generative engines analyze content in terms of relationships, attributes, and informational completeness. They care less about specific phrasing and more about whether the content represents knowledge in a structured, coherent way.

ChatGPT, for example, produces summaries based on patterns within its training data. When it encounters a topic, it identifies core entities, supporting concepts, common questions, and authoritative context. If a webpage expresses these clearly, it’s more likely to surface as a source or influence the generated answer. The same is true for Perplexity, which often cites sources directly; pages with distinct structure, strong contextual language, and clear semantic signals tend to appear more frequently.

SGE (Search Generative Experience) operates similarly by blending AI summaries with traditional search results. It leans on semantic interpretation, entity relationships, and contextual breadth. This means that pages optimized for old-school “keyword lists” often fail to contribute meaningfully to AI-generated responses because they lack the depth these systems depend on.

This shift creates a different requirement for content creators. Rather than focusing on keywords alone, you must consider how your content would appear if rewritten by an AI. What elements would the engine highlight? What entities or relationships would it extract? How would it interpret your explanations? These questions guide a more modern approach to semantic optimization, one far more aligned with today’s systems than the outdated LSI mindset.

Even elements like writing clear and useful meta descriptions contribute to how these systems interpret your page’s context.

Practical Ways To Use Related Terms Without Chasing LSI Lists

Writers often overthink semantic optimization, assuming it requires sophisticated tools or technical techniques. In reality, when you understand a topic well and write comprehensively, the necessary related terms tend to appear naturally. That said, a structured method helps ensure your content captures the full context of the subject.

One of the simplest methods is to outline your content around user intent. Ask what readers need to know, what concerns they have, and what supporting concepts clarify the main idea. By addressing these questions directly, you incorporate meaningful vocabulary without forcing anything. This also ensures your content remains aligned with the actual expectations of searchers.

Another useful approach involves studying top-ranking pages not to copy keywords but to analyze coverage. What subtopics do they include? How do they explain relationships or causes? Which entities appear consistently across multiple authoritative pages? These patterns offer clues about what matters to the topic and help you understand which concepts should also be included in your work, provided they add value.

Writers can also benefit from expanding explanations with examples or brief comparisons. When you provide an example, you naturally introduce related concepts and vocabulary. For instance, explaining why “apple varieties differ in acidity” brings in terms like sweetness, pH, and storage life, even though you never aimed to include them as keywords. These organic additions help search engines build a more complete picture of the content.

The key is not to create artificial lists of LSI terms but to understand the topic deeply enough that context appears without effort. By focusing on clarity, usefulness, and coverage, you achieve what LSI lists promised, but with far better results and without mechanical repetition.
The goal is not mechanical repetition but thoughtful keyword placement that supports clarity and user intent.

Methods and Tools for Identifying Semantic Signals

While you can write semantically rich content without tools, certain methods offer deeper insights into the relationships and entities surrounding your topic. Topic modeling, for example, can reveal subtopics that consistently appear across authoritative sources. This can help identify gaps in your content or guide the structure of a long-form page.

Entity extraction tools can also be helpful. They analyze text and highlight the entities Google is likely to recognize. By comparing your page with top-ranking competitors, you can see which entities you cover and which you may have missed. This does not mean you should add every entity mentioned elsewhere, but it does help confirm that your content aligns with the broader context readers expect.

Another practical method is to review People Also Ask questions and related searches. These give insight into the topics and attributes users commonly associate with the subject. While this doesn’t replace deeper research, it provides a sense of the informational landscape and helps ensure your content addresses common gaps.

Traditional keyword tools still have value as well, though in a different way than before. Instead of using them to build LSI lists, you can examine keyword clusters to understand user intent variations. This helps you shape the flow of your content and include relevant phrases where appropriate, not because they’re required but because they reflect real user behavior.

DefiniteSEO’s tools add another layer by analyzing content for contextual signals, internal linking opportunities, and clarity. These tools help writers understand whether the content communicates meaning effectively and whether the page is structured in a way that supports semantic interpretation. When used thoughtfully, these methods guide you toward more authoritative, coherent content without relying on outdated LSI advice.

Examples: Strong Semantic Optimization vs Mechanical Keyword Use

Sometimes the easiest way to understand semantic optimization is to see the contrast between natural writing and forced keyword insertion. Consider a writer who follows an LSI keyword list. They might create paragraphs filled with synonyms and vaguely related terms, hoping the sheer volume improves relevance. The result usually feels cluttered, repetitive, and unfocused. Search engines see this as thin content wrapped in unnecessary noise.

A stronger approach starts with clarity and purpose. If a page explains “how to improve soil quality,” it might naturally discuss nutrients, pH balance, organic matter, drainage, compost, and microbial activity. These concepts inform the reader and provide context that search engines recognize. The writer does not need to force synonyms like “earth quality,” “soil enhancement,” or “garden dirt improvement.” Instead, they focus on the real relationships and processes that matter to the topic.

Another example involves product comparisons. A poorly optimized page might repeatedly insert synonyms, trying to satisfy a list of LSI terms. But a well-optimized comparison focuses on features, performance, user needs, testing results, and differentiators. These elements contain rich semantic signals because they describe the subject matter in detail.

Semantic optimization shows up in how you structure explanations, not how many rephrased terms you include. Clear, meaningful, illustrative writing produces the contextual richness that engines reward. When semantic signals appear organically, readers feel more engaged and search systems interpret your content more accurately.
Visual content contributes to semantic clarity as well, especially through image alt text and surrounding context, which help engines identify entities connected to the page.

Common Myths About LSI and SEO

Despite years of clarification from search engine representatives, several myths about LSI persist. One of the most common is the belief that Google directly uses latent semantic indexing in its ranking algorithms. In reality, Google has consistently stated that LSI is not part of its systems. Modern NLP techniques have moved far beyond what LSI could ever accomplish.

Another myth claims that adding LSI keywords automatically improves rankings. While including related terms can indirectly help by improving topical depth, rankings do not increase because search engines reward LSI lists. The actual improvement comes from better coverage and clearer communication, not from keyword variations themselves.

Some SEO advice suggests you need a specific number of LSI terms per page. This idea leads to formulaic writing and distracts from the real task: addressing user needs. No modern search engine evaluates content based on how many synonyms or supporting phrases appear.

There is also a belief that LSI keywords can “fix” thin content. But adding a handful of related terms does nothing to compensate for shallow explanations or missing sections. Search engines evaluate depth, quality, and usefulness. LSI keyword lists do not transform weak content into strong content.

Understanding these myths helps writers avoid misleading practices. The goal is not to chase LSI but to adopt a holistic approach to meaning, structure, and coverage, the aspects that genuinely improve visibility across Google, Bing, and AI search engines.

How DefiniteSEO Helps Improve Semantic Optimization

Writers often understand their topics well but still struggle to communicate context in a way that satisfies both readers and modern search systems. This is where structured tools can help. DefiniteSEO’s platform supports semantic optimization while keeping the focus on clarity and intent rather than keyword mechanics. It evaluates your page structure, internal linking, and contextual signals to show where meaning may be unclear or underdeveloped. Semantic clarity improves further when your site maintains a strong internal linking structure that helps search engines connect related concepts.

For example, the SEO Checker highlights gaps in coverage by comparing your content with expected entities or subtopics. This doesn’t mean it produces LSI lists. Instead, it flags areas where a topic might feel incomplete from the perspective of both readers and search engines. The WordPress Plugin extends this workflow by guiding creators as they write, offering suggestions for improved readability, natural contextual development, and on-page structure.

These tools do not replace the need for expertise, thoughtful research, or strong writing. They simply make it easier to ensure that your content communicates context effectively. In an environment where search models value meaning above surface-level signals, that clarity becomes an advantage across traditional search and AI-generated answers.

Conclusion

The debate around LSI keywords often distracts from the real priorities of modern SEO. While the phrase continues to appear in many guides, it represents a past era of search where keyword matching dominated. Today’s engines operate on a deeper understanding of language, intent, and entities. They need content that communicates meaning clearly, not lists of synonyms or loosely related terms.

Strong semantic optimization is not about forcing vocabulary or following formulas. It’s about understanding the topic fully, anticipating user needs, and explaining concepts with clarity. When writers approach content with this mindset, they naturally incorporate the relationships and entities that modern systems rely on. This leads to higher relevance, stronger authority signals, and better performance across Google, SGE, ChatGPT-style answers, Perplexity, and other emerging engines.

By letting go of outdated LSI theories and embracing meaningful contextual writing, creators position their content for both present and future search environments. The goal hasn’t changed: help users find trustworthy, useful information. What has changed is how engines understand that information and how content must adapt in response.

FAQs

1. Does Google use LSI as part of its ranking algorithm?

No. Google has confirmed multiple times that latent semantic indexing is not used in its ranking systems. Modern search relies on advanced language models, entity understanding, and contextual interpretation.

2. Should I still use LSI keyword lists from SEO tools?

You can use them as inspiration, but not as rules. Treat them as hints for related concepts rather than items you must include. Focus on covering the topic thoroughly instead of matching a list.

3. Are entities more important than keywords now?

Entities and keywords work together. Keywords help identify the topic, while entities and relationships establish depth and clarity. Both matter, but entities drive modern semantic understanding.

4. How can I avoid over-optimizing for related terms?

Write for clarity first. If a sentence feels forced or repetitive, cut it. Cover the topic in a natural, structured way. Over-optimization usually happens when writers try to satisfy keyword formulas.

5. How do AI engines decide what to include in summaries?

AI systems extract the clearest, most representative information from content. They look for well-explained ideas, defined entities, logical structure, and content that demonstrates topical completeness.

6. Can related terms still help rankings even if LSI is outdated?

Yes, but only when they add context. Related terms improve rankings by strengthening topical depth and clarity, not because search engines look for LSI patterns.

7. Is semantic optimization different for AI search vs Google?

The principles are similar. Both value clarity, depth, structure, and entity relationships. AI engines lean even more heavily on factual context and completeness.