When I first started using LSI keywords and related keyword generators, I thought SEO was largely a numbers game.
Find the main keyword. Generate as many relevant variations as possible. Then make sure those variations appear throughout the article.
If a tool gave me 20 or 30 related terms, I treated them almost like a checklist. I assumed that the more of those terms I could naturally fit into an article, the better Google would understand the topic.
That wasn’t completely useless. Keyword research did help me discover how people searched.
But my mental model was wrong.
Over time, I realized that the real value of a related keyword isn’t the word itself. It’s what that search tells you about the person behind it.
Today, I don’t use an LSI or related keyword generator as a list of phrases I need to insert into an article. I use it as a research tool for discovering topics, questions, terminology, search intent, and potential content gaps.
That distinction changes everything.
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What are LSI keywords actually supposed to do?
The first problem is the term “LSI keywords.”
In SEO, the phrase is commonly used to describe words and phrases that are semantically related to a primary topic. But I don’t think it’s a particularly useful mental model for modern SEO.
People often hear “LSI keywords” and assume there is a special category of keywords they need to find and place throughout an article to make Google rank it.
That’s the wrong question.
If someone tells me they need to put 20, 30, or 50 LSI keywords into every article, my first response would be:
Why?
There isn’t a meaningful SEO rule saying that an article needs a particular number of related keywords.
If you genuinely understand a subject, related terminology and concepts will often appear naturally.
For example, if I’m writing about trekking in Nepal, I don’t need a keyword list telling me to mention altitude, permits, accommodation, weather, transportation, difficulty, preparation, and cost.
Those are simply important parts of understanding the subject.
The useful question isn’t:
“What 30 keywords can I add?”
It’s:
“What does someone researching this topic actually need to know?”
That was one of the biggest changes in how I approached SEO.
I stopped seeing keywords as words I needed to place and started seeing them as clues about the searcher.
My biggest lesson: related doesn’t automatically mean relevant
One of the mistakes I made early on was assuming that a keyword suggestion was valuable simply because it was closely related to my main keyword.
Travel content made this particularly obvious.
Suppose I’m creating content around a destination. A keyword tool might give me several queries containing the destination name. On the surface, they look perfectly relevant.
But when I actually search those queries, I sometimes find that Google is showing a completely different type of page.
That’s when I started checking the search results instead of blindly trusting the keyword tool.
A keyword generator can tell you that two queries are associated.
It doesn’t necessarily tell you why they’re associated.
One query might be informational. Another might have commercial intent. Another might be looking for a specific service. Another might be asking for directions or booking information.
The keywords are related.
The pages they require may not be.
This led to one of the rules I use today:
A keyword can be semantically related to your topic and still be completely wrong for your page.
If I had blindly followed every suggestion, I would have created unnecessary sections just to include keywords.
The article would have become longer without becoming better.
In some cases, it could even have pulled the article toward a different search intent and made the page less focused.
So when I see a related keyword, I don’t immediately ask how to use it.
I ask whether it belongs.
A real example from my Nepal travel content
One of the clearest examples comes from working on content around Nepal travel experiences, including Lumbini, Buddhist heritage travel, and Himalayan trekking.
Earlier, I might have approached a topic like “Lumbini Nepal” primarily as a keyword target.
My goal would have been to create content around that phrase and include relevant variations.
Now I approach it differently.
I look at what a person actually needs if they’re researching Lumbini.
That can include things such as:
- Things to do in Lumbini
- Buddhist heritage sites
- How to visit Lumbini
- Nearby attractions
- Best time to visit
- Travel information
- Questions a first-time visitor is likely to have
The important change isn’t that I added more keywords.
It’s that the related searches changed the structure of the article.
Instead of repeating “Lumbini Nepal” in different forms, I could build content around the actual information a traveler needs.
I don’t claim a specific traffic increase from this process because I didn’t properly record one, and I don’t think it’s useful to invent a number after the fact.
What I did observe was that the pages became much more comprehensive and had the potential to appear for variations of the original search rather than being dependent on one exact phrase.
The improvement came from better topic coverage and relevance, not from reaching an arbitrary keyword count.
How I actually use a related keyword generator
My workflow doesn’t start with the generator.
It starts with the searcher.
1. Search the primary keyword yourself
Before opening a keyword tool, I search the primary keyword.
I want to understand:
- What is the dominant search intent?
- What kind of pages are ranking?
- What questions are being answered?
- What information appears consistently important?
- Are there multiple interpretations of the query?
This gives me context.
Without it, a keyword list is just a spreadsheet full of words.
2. Collect ideas from multiple sources
Once I understand the query, I start collecting related ideas.
Depending on the topic, I might use:
- Google autocomplete
- People Also Ask
- Related searches
- Competitor pages
- Keyword research tools
- Google Search Console
- Reddit and other forums or communities
I don’t expect any single source to give me the complete picture.
Google can reveal questions.
Competitors can reveal commonly covered subtopics.
Forums can reveal how real people describe their problems.
Search Console can reveal what people actually searched before landing on my existing content.
The sources complement each other.
3. Don’t obsess over collecting hundreds of keywords
For a normal article, 20 to 50 useful suggestions can be more than enough to start.
I don’t consider that a target.
If a large topic requires hundreds of queries for proper research, I’ll collect them. But I don’t believe that the person with the biggest keyword spreadsheet automatically has the best SEO strategy.
The list is research material.
It isn’t a content requirement.
4. Remove the garbage
This is where judgment becomes important.
If I have 50 suggestions, I ask three questions:
Is it actually relevant to the main topic?
Does it represent a useful piece of information or a real search intent?
Would including it make the article more useful?
That last question is probably the most important.
I remove duplicates, different intents, irrelevant locations, unrelated meanings, queries aimed at another audience, and suggestions that simply don’t improve the page.
I’ve become much more comfortable throwing keywords away.
That’s a good thing.
Group keywords by meaning, not alphabetically
Suppose I’m researching a Nepal trekking article and discover:
- Nepal trek difficulty
- Difficulty of trekking in Nepal
- How difficult is Nepal trekking
- Best time to trek
- Best season for trekking
- When to trek
- Nepal trekking cost
- Trekking prices in Nepal
I don’t want eight separate sections.
Several of these queries represent essentially the same underlying intent.
I group them.
For example:
Difficulty
Best time
Cost
Altitude
Permits
Accommodation
Transportation
Preparation
Now I’m not building an article around eight keyword variations.
I’m building an article around the questions a traveler actually needs answered.
This also prevents one of the most common problems with SEO content: bloated structure.
When should a related keyword become an H2?
My rule is simple:
If it deserves a meaningful explanation on its own, it can become a section.
If a topic requires several paragraphs, examples, data, or supporting information, an H2 may make sense.
If it’s a narrower component of a larger topic, it can become an H3.
If it’s a specific question that readers genuinely ask, it might become an FAQ.
And if it can be answered naturally inside another section, I don’t give it its own heading at all.
For example, “best time to trek,” “best season for trekking,” and “when to trek” probably don’t deserve three headings.
They represent the same information need.
On the other hand, altitude sickness might deserve its own subsection if it is an important concern for someone planning that particular trek.
The structure should follow the reader’s journey.
A useful test I use is:
If I remove this keyword, does the reader lose something useful?
If yes, investigate what information is missing.
If no, and I’m only keeping it because a tool suggested it, remove it.
Don’t force exact-match keywords into your writing
This is another lesson I learned through experience.
Earlier in my SEO work, I placed more importance on making sure exact keyword variations appeared in the content.
Today, I care much more about communicating the concept naturally.
If several queries mean essentially the same thing, I don’t need to repeat every phrase.
The reader doesn’t care whether I’ve successfully included the keyword variation.
They care whether I answered their question.
This is why I don’t recommend turning headings into awkward combinations of keywords.
A heading should tell the reader what they’re about to learn.
The related searches can influence the section without appearing word-for-word in the heading.
Search Console changed how I think about keyword research
Keyword research shouldn’t necessarily stop when an article is published.
Once a page has accumulated enough data, Google Search Console becomes extremely useful.
I look at:
- Impressions
- Clicks
- CTR
- Average position
- Queries generating impressions
- Queries generating clicks
- Changes over time
But the most interesting part for this particular process is often the queries.
Sometimes a page starts receiving impressions for searches I never deliberately targeted.
That’s valuable information.
But I don’t automatically add those queries to the article.
Instead, I ask:
Is this query actually relevant to the page?
If it is, I investigate whether the article properly answers the underlying question.
If there’s a genuine content gap, I may expand the page.
If the existing article already answers it, I might leave it alone.
And if the query represents a completely different intent, I don’t force it into the page.
I’ve come to think of unexpected queries as evidence to investigate, not keywords to insert.
That’s an important difference.
Search Console can effectively become part of your next round of keyword research.
Before publishing, you’re making an educated prediction about what people want.
After publishing, you have real search data that can challenge that prediction.
The biggest mistakes I see with LSI keyword generators
The mistakes generally come back to treating the tool as the authority.
Trusting one tool
No keyword tool has a complete understanding of a topic.
I prefer comparing different sources and looking for patterns.
Chasing search volume
A high-volume keyword isn’t automatically valuable for my article.
A smaller query with exactly the right intent can be much more useful than a high-volume query that belongs on another page.
Creating a section for every keyword
This creates bloated content.
Group similar queries by their underlying meaning.
Ignoring search intent
This is probably the most damaging mistake.
Always check what the searcher actually wants and what kind of results Google provides for the query.
Forcing exact phrases
If an exact-match keyword makes the sentence awkward, don’t force it.
Write naturally and cover the concept properly.
Copying competitor keyword lists
Competitors are useful for research, not imitation.
Look for patterns and understand why certain topics are covered.
Over-optimizing headings
Don’t turn headings into keyword combinations just because a tool suggested several variations.
Clarity comes first.
Never revisiting the research
Search behavior can reveal opportunities after publication.
Use Search Console data to find genuine gaps, not to mechanically insert every query that generates an impression.
Where AI fits into related-keyword research
AI is going to make this process much faster.
But I don’t think its biggest contribution will be generating more keywords.
In fact, I think generating enormous keyword lists is becoming less valuable.
The interesting application is what AI can do with the data.
For example, instead of asking AI for 500 related keywords, I’d rather give it a large dataset and ask it to classify the queries into:
Same intent → related subtopic → separate search intent → FAQ → irrelevant
That can save an enormous amount of manual work.
AI can also help identify:
- Duplicate keyword variations
- Search-intent clusters
- Questions
- Potential content gaps
- Topic relationships
- Entity relationships
- Competitor coverage patterns
- Search Console query patterns
- Potential content clusters
But there’s an important danger.
AI can manufacture the illusion of relevance.
Ask an AI to generate 1,000 related keywords and you’ll probably get an impressive list.
That doesn’t mean the list is useful.
Some phrases will be redundant. Others will have different intent. Some may only be weakly associated with the topic. Others might be unnatural expressions that nobody actually uses.
AI could therefore make the old LSI problem worse.
Instead of a human stuffing 50 keywords into an article, we could end up with AI generating 500 “semantically related” concepts and another AI trying to cover all of them.
The result could be comprehensive on paper but terrible for the reader.
That’s why I see AI as most useful for interpretation, organization, and discovery.
Humans still need to decide what deserves to exist on the page.
My practical workflow for using an LSI or related keyword generator
If I had to give someone a process they could follow today, it would be this:
1. Understand the primary keyword.
Search it yourself and determine the dominant intent.
2. Study the current results.
Look at the questions, page types, topics, and information Google is surfacing.
3. Collect related ideas.
Use autocomplete, People Also Ask, related searches, competitors, keyword tools, Search Console, and relevant communities.
4. Treat the list as raw research.
Don’t assume every suggestion belongs in your article.
5. Filter aggressively.
Remove duplicates, irrelevant queries, different intents, and anything that doesn’t help the reader.
6. Group by meaning.
Cluster queries around underlying topics and information needs.
7. Map the groups to the content.
Decide what deserves an H2, H3, FAQ, supporting paragraph, separate article, or nothing.
8. Write naturally.
Use the language that communicates the concept clearly. Don’t force exact-match phrases.
9. Publish and collect evidence.
Use Search Console to understand which relevant queries are generating impressions and clicks.
10. Improve based on evidence.
Expand genuine gaps and ignore unrelated queries.
The process is essentially:
Research → organize → write → publish → observe → improve → repeat.
The future isn’t about generating more keywords
I think related-keyword research is gradually becoming less about keyword collection and more about understanding search intent and topic relationships.
AI will make it easier to process enormous amounts of search data.
It will be able to cluster queries, identify entities, compare content, find gaps, and build topic maps much faster than a person working through a spreadsheet.
But that doesn’t eliminate human judgment.
It makes it more important.
The competitive advantage won’t necessarily belong to whoever can generate the most keywords.
It will belong to whoever can take messy search data and make better decisions about:
What deserves to be covered?
What deserves its own section?
What belongs in another article?
What represents a different search intent?
And what should simply be ignored?
Final takeaway
If there’s one thing I want you to remember, it’s this:
Don’t ask, “How many related keywords can I put into this article?” Ask, “What does this search data tell me that my reader needs to know?”
That is the fundamental difference between keyword-driven writing and topic-driven SEO.
An LSI or related keyword generator can give you hundreds of words.
AI can give you thousands.
Neither automatically tells you what deserves to be published.
The valuable skill is knowing what to keep, what to combine, what to explain, what to turn into another piece of content, and what to throw away.
So I wouldn’t tell you to stop using keyword generators.
I’d tell you to use them as discovery tools, not writing instructions.
Start with the searcher. Understand the intent. Collect related questions and concepts. Group them by meaning. Build the article around what the reader actually needs.
Then let real search data tell you what you missed.
The technology will keep changing. The principle won’t:
Understand what people are looking for, create the best answer you can, and use evidence to make it better over time.