What Are LSI Keywords?
LSI keywords — short for Latent Semantic Indexing — is the SEO term for words and phrases that are semantically related to your main topic. If your page is about "running shoes," terms like trainers, sneakers, cushioning, gait analysis, marathon, and pronation are LSI keywords. They signal to a search engine, or an AI engine, that your page covers the topic with real depth instead of just repeating one phrase.
Important nuance: Google's John Mueller has publicly stated "there is no such thing as LSI keywords" — Google does not use the original 1988 Latent Semantic Indexing math from Bell Labs (Deerwester et al.). Modern engines use BERT, MUM, and word embeddings instead. But the underlying idea — that broad, semantically-related vocabulary signals topical relevance — is exactly what those modern systems reward. So we keep the LSI label for familiarity and treat it as shorthand for "semantic and related keywords." This metric is part of the Content Quality pillar in your GEO-Score.
Why Semantic Keywords Matter for AI Search
AI engines like ChatGPT, Perplexity, and Google AI Overviews do not match strings of letters anymore. They convert your text into vector embeddings and compare it to query embeddings. Pages that cover a topic from multiple angles end up close to many query vectors — which is exactly when they get cited.
Topical depth beats keyword repetition
An Ahrefs study found the average top-ranking page also ranks for around 1,000 related keywords — not because it repeats one phrase, but because it covers the topic broadly. Pages thin on semantic vocabulary look shallow to both Google and AI engines and get skipped in favor of more comprehensive sources.
Engines reason about entities, not strings
Since Hummingbird (2013) and BERT (2019), Google reasons about entities — people, places, products, concepts — and the relationships between them. Bill Slawski's patent research at SEO by the Sea showed Google uses Knowledge Graph entities and co-occurring terms to verify a page is genuinely about a topic. Semantic vocabulary is what triggers that recognition.
Semantic breadth fuels AI Overview citations
An Ahrefs analysis of 4M AI Overview URLs found broader topical coverage strongly correlates with citation likelihood. Sites with well-developed topic clusters and semantically-rich content see up to 30% higher citation rates in AI Overviews compared to thin, single-keyword pages.
What the Research Says
There is no such thing as LSI keywords — anyone who is telling you otherwise is mistaken, sorry.
— John Mueller, Google Search Advocate, public statement (2019). The mathematical LSI from Deerwester et al. (1988) is not used by Google. Modern engines use BERT, MUM, and word embeddings — but the practical principle of semantic breadth still applies.
The average top-ranking page also ranks in the top 10 for nearly 1,000 other relevant keywords. Pages do not rank for one phrase — they rank for a cloud of semantically-related queries because they cover a topic, not a keyword.
— Ahrefs, How Often Top-Ranking Pages Also Rank for Related Keywords, ranking study (3M+ search queries analyzed)
We analyzed 863,000 keyword SERPs and 4 million AI Overview URLs. Topical authority — measured by the breadth of related keywords a domain ranks for — was the strongest single predictor of AI Overview citations, with a correlation of r = 0.41.
— Ahrefs, AI Overview Citation Patterns Study, 2026 (4M URLs analyzed across 863K SERPs)
Real Examples: Single-Keyword Stuffing vs. Semantic Coverage
The clearest way to show this: take three real-world pages and look at the vocabulary they actually use. Pages that pile up one phrase get skipped by both Google and AI engines. Pages that draw on the full semantic field get cited.
Example 1: Blog post about "running shoes"
"Looking for the best running shoes? Our running shoes guide reviews the top running shoes of 2026. We tested running shoes for road running, trail running, and beginner running. The best running shoes are the running shoes that fit your running style. Buy running shoes today."
Why this fails: "running shoes" appears 9 times in 50 words (18% density of one phrase). No vocabulary depth — no mention of cushioning, drop, gait, pronation, trainers, sneakers, marathon, or any other term a real expert would use. Google's spam policy explicitly lists "repeating the same phrases unnaturally" as keyword stuffing.
"Choosing the right running shoe depends on your gait, foot strike, and weekly mileage. Neutral runners with a midfoot strike often pick max-cushion trainers like the Hoka Clifton 9 (8mm drop, 32mm stack). Overpronators benefit from stability sneakers with a guide rail or medial post. Trail runners need lugged outsoles for grip on technical terrain, while marathoners often choose carbon-plated racing shoes for propulsion."
Why this works: "running shoes" never repeats — but the page is unmistakably about running shoes. Terms like gait, foot strike, midfoot, drop, stack, overpronator, stability, medial post, lugged outsole, and carbon-plated tell BERT and modern AI engines this is expert content. It will rank for hundreds of related queries.
Example 2: Product page for an "ergonomic office chair"
"Buy our ergonomic office chair. This ergonomic office chair is the best ergonomic office chair for any office. Our ergonomic office chair has all the features you need in an ergonomic office chair. Order your ergonomic office chair now."
Why this fails: 36 words, 6 repetitions of "ergonomic office chair" (16% density). Zero descriptive vocabulary. An AI assistant asked "which chair has good lumbar support for a tall person?" cannot extract anything from this page because the page never mentions lumbar, height, support, or any feature.
"Our task chair pairs adjustable lumbar support with a 4D armrest, breathable mesh back, and a synchro-tilt mechanism that follows your spine through reclines from 90 to 135 degrees. The seat depth slides 70mm for users between 5'2" and 6'5". A class-4 hydraulic cylinder supports up to 300 lbs and meets BIFMA durability standards."
Why this works: One natural mention of "task chair" plus rich vocabulary — lumbar support, 4D armrest, mesh back, synchro-tilt, recline, seat depth, hydraulic cylinder, BIFMA. The page now answers dozens of related questions and shows up for long-tail queries like "chair with adjustable seat depth for tall users."
Example 3: B2B SaaS page about "data observability"
"Data observability is critical for data observability. Our data observability platform delivers data observability across your data observability stack. Get data observability today with our data observability tools designed for modern data observability needs."
Why this fails: "Data observability" is a real B2B term, but repeating it 8 times in 35 words is spam. The page also misses the semantic neighborhood: a CTO researching this topic uses words like data quality, lineage, freshness, anomaly detection, schema drift, SLA, dbt, Snowflake, Monte Carlo, OpenLineage. None of those appear, so the page never gets cited by ChatGPT for technical queries.
"Data observability gives data teams end-to-end visibility into pipeline health: freshness, volume, schema drift, lineage, and distribution anomalies. Unlike traditional monitoring, observability covers the five pillars from Monte Carlo's framework — and integrates natively with dbt, Snowflake, BigQuery, and Airflow via OpenLineage. Common alerts include null spikes, late-arriving data, and unexpected schema changes upstream."
Why this works: One canonical mention of "data observability" plus the full entity field — freshness, volume, schema drift, lineage, anomalies, Monte Carlo, dbt, Snowflake, OpenLineage, Airflow. ChatGPT now confidently cites this page when asked about data quality, pipeline monitoring, or schema drift detection.
How to Cover a Topic Semantically
Do NOT Do This
- ✗Repeat your exact target phrase more than once every 200-300 words — Google's spam policy lists this as keyword stuffing and modern AI engines simply skip it
- ✗Use only the exact target phrase and ignore obvious synonyms (e.g., "sneakers" alongside "trainers," "laptop" alongside "notebook") — engines penalize vocabulary that looks unnaturally narrow
- ✗Force in every term an "LSI keyword tool" suggests, even when it does not fit — engines detect awkward, unnatural co-occurrence patterns
- ✗Skip the named entities of your topic — products, brands, frameworks, standards, people, places. Without them BERT cannot map your page to the Knowledge Graph
- ✗Pad pages with thin filler synonyms instead of substantive coverage — broad vocabulary without real information still loses to a deeper, denser competitor
Do This Instead
- ✓Write as if explaining the topic to an expert in the field — they naturally use the full semantic vocabulary (jargon, units, standards, brand names) without thinking about it
- ✓Open the top 10 ranking pages for your target query and list every recurring term they use — this reveals the topic's real semantic neighborhood (the SEO method behind tools like Surfer, Clearscope, and Frase)
- ✓Include the named entities — products, organizations, standards, people, geographic places — so Google can connect your page to its Knowledge Graph
- ✓Use synonyms, abbreviations, and plurals naturally ("running shoes" / "trainers" / "sneakers"; "AI" / "artificial intelligence") — Backlinko's research shows Google treats these as the same intent
- ✓Build a topic cluster: one pillar page covering the topic broadly, plus 5-15 supporting pages on subtopics. HubSpot's data shows topic-cluster sites average 43% higher organic traffic and significantly higher AI citation rates
Quick Tips for Semantic Coverage
- •Spend 15 minutes reading the top 10 SERP results before writing — note every recurring noun and verb. That list is your semantic checklist.
- •Use your exact target phrase no more than 1-2x per 300 words. Past that, switch to synonyms, related terms, or pronouns.
- •Name at least 3 specific entities per article — a product, a company, a standard, a person, a place. Entities feed the Knowledge Graph.
- •Use Surfer, Clearscope, Frase, or even Google's "People also ask" and "Related searches" to surface semantic terms — but ignore any that do not fit naturally.
- •Group related articles into clusters with internal links. HubSpot found topic-cluster sites grow organic traffic ~3.2x faster on average.
- •Read each paragraph aloud. If it sounds like a human expert wrote it, the semantic vocabulary is probably already there. If it sounds robotic, you are stuffing.
Frequently Asked Questions
Does Google actually use LSI keywords?
What is the difference between LSI keywords, semantic keywords, and related keywords?
How do I find semantic keywords for my topic?
What about keyword density — is the old 1-3% rule still valid?
Will adding more semantic keywords help me get cited by ChatGPT and Perplexity?
Can I just use an AI writer to generate semantic content?
Related Metrics to Explore
- Comprehensiveness
Semantic keywords are the vocabulary; comprehensiveness is the depth of coverage. Together they signal that your page genuinely covers the topic.
- Topical Authority
Ahrefs found topical authority (breadth of related keyword rankings) is the single strongest predictor of AI Overview citations. Build it with topic clusters.
- Semantic Clarity
Even with rich vocabulary, sentences need to be unambiguous. Semantic clarity ensures BERT and AI engines correctly extract meaning from your text.
- Knowledge Graph
Named entities (products, brands, people, places) connect your page to Google's Knowledge Graph — the structured backbone behind both AI Overviews and ChatGPT.