Is keyword density still a thing for AI search or is it completely irrelevant now?
Community discussion on whether keyword density matters for AI search. Real experiences from SEO professionals testing keyword optimization impact on ChatGPT an...
Having an internal debate about writing style for AI search.
The old approach:
What I’m hearing about AI:
My confusion:
As a writer, I need to know: am I writing for robots or people?
Let me explain NLU in practical terms.
What NLU means for AI search:
AI systems today can:
Example:
User asks: “What’s a good tool for tracking customers?”
AI understands this means:
It doesn’t need content to say “tracking customers” exactly.
What this means for writing:
DON’T: “Looking for a tool for tracking customers? Our customer tracking tool helps you track customers effectively.”
DO: “A CRM system helps you manage customer relationships, track interactions, and organize your sales pipeline effectively.”
Both answer the query. The second is natural. AI understands both, but prefers the natural one.
The answer to your question:
Write for humans. AI’s NLU is sophisticated enough to understand good human writing. In fact, it’s trained on human writing.
The robotic style was for older algorithms. Modern AI reads like a human.
Keywords still matter, but differently.
Old keyword role:
New keyword role:
The practical balance:
You should:
You shouldn’t:
Example:
Topic: CRM software
Include naturally:
But don’t:
The test:
Read your content aloud. Does it sound like an expert talking? That’s good for NLU.
Does it sound like SEO content? That’s a warning sign.
The writer’s practical guide to NLU-friendly content.
Structure that works:
1. Clear question as heading:
## How does CRM software improve sales?
AI recognizes question-answer format.
2. Direct answer first:
CRM software improves sales by organizing customer
data, automating follow-ups, and providing pipeline
visibility that helps teams close deals faster.
AI can extract this as a citable statement.
3. Depth follows: Expand on the answer with examples, data, nuance.
Natural language signals:
Good for NLU:
Bad for NLU:
The editing test:
After writing, edit for:
NLU-friendly = reader-friendly.
Data perspective on writing style and AI citations.
What we analyzed:
1,000 pieces of content, half traditional SEO style, half natural style.
Results:
Traditional SEO style:
Natural style:
The pattern:
Higher keyword density correlated with LOWER citation rates.
Higher readability correlated with HIGHER citation rates.
Why this makes sense:
AI systems are trained on quality human writing. They recognize and prefer:
Stuffed content reads as low quality - because it is.
The conclusion:
Data supports: write naturally, get cited more.
Editorial perspective on the shift.
What we tell writers now:
“Write as if you’re explaining to a knowledgeable peer.”
Not: “Write for Google.” Not: “Include X keyword Y times.”
The brief evolution:
Old brief:
New brief:
The quality improvement:
Ironically, removing keyword requirements improved content quality. Writers focus on value, not counting words.
The AI bonus:
Better content for readers = better content for AI. They want the same thing.
Technical writing perspective.
Precision still matters:
NLU means AI understands context. But:
Where NLU helps technical content:
AI can understand:
But AI still needs:
Clear explanations for complex concepts. Don’t assume NLU means AI knows everything.
The balance:
Write naturally, but:
Example:
“Kubernetes (K8s) is an open-source container orchestration platform that automates deployment, scaling, and management of containerized applications.”
Natural, but precise. NLU understands it. Readers do too.
User research perspective.
What users actually want:
What AI wants to provide:
Exactly the same thing.
The alignment:
AI’s job is to connect users with helpful content. It’s trained to recognize content that helps users.
Writing for users = writing for AI.
The user-first approach:
Before writing, ask:
Answer those, and you’ve optimized for NLU.
The anti-pattern:
Writing that prioritizes algorithm signals over user value. AI increasingly recognizes and deprioritizes this.
This thread confirmed what I hoped was true.
My takeaways:
What I’m changing:
Stopping:
Continuing:
The liberating truth:
Writing good content for readers IS writing for AI. I can focus on my craft, not SEO tricks.
Thanks for the clarity!
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