How to use AI to write articles that will push your site to the TOP of Yandex and Google
Do you want to know why your articles don’t make it into the TOP, despite perfect keyword placement? You are not alone. In 2026, classic SEO copywriting methods — keyword stuffing, density, exact matches — have stopped working. Using AI to write articles that reach the TOP of Yandex and Google, is now possible in a completely different way: through semantic analysis, entity extraction, and automation with AI agents.
25% of search queries in the US are performed by voice. More than half of Gen Z users use voice search every day. People no longer type “Italian restaurant Profsoyuznaya” — they say: “Find me a restaurant with Italian cuisine and a rating above 4 near the Profsoyuznaya metro station that is open until 23:00.” And search engines have learned to understand meaning, not keywords.
In this guide, you will learn:
- Why keywords no longer affect rankings
- What entities, triplets, and a knowledge graph are
- How AI extracts entities from competitors’ texts
- A step-by-step system for creating articles that shoot into the TOP the moment they are indexed
- Why some AI articles are in the top, while others are not
- Current ranking factors for 2026
Let’s figure out how modern search works and how to use AI to create content that actually ranks.
Why keywords no longer work: the era of semantic search
Ten years ago, SEO specialists used the classic formula: take the keyword “windows Moscow,” add it to the title, repeat it 5–7 times in the text, and get into the TOP. The dominant algorithms — TF-IDF and BM-25 — worked with frequency and occurrences. You could spam keywords, and it worked.
Today, search engines use neural networks (LLMs — large language models) that extract entities, meanings and intents of the user. They build a Knowledge Graph, where each entity is connected to others through triplets and relationships.
This is not a technology of the future. This is how Google and Yandex work right now.
How search queries have changed
Previously, the user typed: italian restaurant profsoyuznaya. Now they hold down the microphone in Google or Alice and say:
“Find me a restaurant with Italian cuisine and a high rating in the area of the Profsoyuznaya metro station that is open until 23:00 — we want to have dinner at ten in the evening.”
The search engine breaks this query into components:
- Intent: find a restaurant (not a zoo, not a hotel)
- Entity type: restaurant
- Attributes: Italian cuisine, rating above 4.0, location near Profsoyuznaya, closing time ≥ 23:00
There will not be an exact match of such a phrase on any page on the internet. Without understanding the meaning of the query, the search engine will not be able to provide an answer.
How a search engine “thinks”
When you send a voice query, Google breaks it down into entities and attributes:
| Attribute | Value |
|---|---|
| Entity type | Restaurant |
| Cuisine | Italian |
| Rating | Above 4.0 |
| Location | Near the Profsoyuznaya metro station |
| Closing time | 23:00 or later |
Next, Google checks the completeness of the match: whether the attributes matched, proximity to the location, data freshness (date of the last update, reviews), and the overall authority signal.
The result is that you see Google’s AI responses (Google AI Mode), which appeared in Russia in August–October 2025, or Alice’s responses in Yandex. Both work on the same logic: entity extraction → matching → ranking.
Key insight: keyword placement is the least important ranking factor in 2026. According to a Surfer SEO study of 10 million queries, the most important factor is Topical Coverage, that is, the completeness of covering the user’s intents.
What entities are and how they work in search
In the semantic web, there are two fundamental concepts: taxonomy and ontology. Together they form the structure on which search engines’ Knowledge Graph is built.
Taxonomy — hierarchy from general to specific
Taxonomy describes the structure of knowledge from a broad concept to a narrow one. A classic example from biology:
- Animals
- Mammals
- Cats
- Domestic cat
- Siamese cat
- Domestic cat
- Cats
- Mammals
Each level narrows the concept. This is exactly how pages are structured on Wikipedia, Wikidata, and on the world’s largest information websites.
Ontology — relationships between entities
If taxonomy is a hierarchy, then ontology is relationships between entities. A simple example:
- A cat is a domestic animal
- A cat eats pet food
- A cat is man’s best friend
- A cat likes milk
- A cat has fur
Each statement is a relationship between two entities. This is exactly how the Google and Yandex knowledge graphs are structured.
Triples: Subject-Predicate-Object
The most important unit in semantic search is triple. It is a relationship model in the format:
Subject → Predicate → Object
| Subject | Predicate | Object |
|---|---|---|
| Cat | likes | Milk |
| Binance | provides | Trading |
| Steve Jobs | founded | Apple |
| iPhone | launched in | 2007 |
| Foundation | supports | House |
Why this matters: when you write an article about foundations, Google expects to see not just the keyword “foundation,” but related entities: foundation type, materials, soil, waterproofing, load-bearing capacity, climate conditions. Without these entities, the article will not be considered complete in the eyes of the search engine.
Where to get entities
- Wikipedia and Wikidata — open sources of structured knowledge
- Google Knowledge Graph — Google’s knowledge graph (available via API)
- Scientific Google Knowledge Graph — an expanded version for researchers
- Reddit and YouTube — analyzing discussions and videos helps identify real entities
How search engines process content: from crawling to vectorization
To understand how to write articles for the top results, you need to know what happens to your content inside the search engine.
Step 1: Crawling
A search robot (crawler) from Google or Yandex downloads documents from your site. These can be HTML pages, JSON files, PDF documents, text files. Search engines are multimodal — they can recognize even video content.
Step 2: Chunking (splitting into fragments)
Each downloaded document is split into chunks — small semantic fragments.
Step 3: Vectorization (embedding)
Each chunk is translated into vector format — a multidimensional numerical representation of meaning.
How does a search engine determine word similarity? Using cosine similarity (cosine similarity):
- “Cat” and “kitten” — small angle, high similarity score (for example, 0.92)
- “Cat” and “brick” — large angle, resulting vector is small (for example, 0.15)
Practical threshold
For SEO: filter out only those entities whose cosine similarity is above 0.89. Everything else is noise.
Pro Tip: An embedding model from OpenAI (text-embedding model) works quite well. The results can even be calculated in Excel.
Step-by-step system: how to write articles for the top 10 with AI
Step 1: Analyze competitors from the top 10
Take the top 10 (or top 20) pages for your query. Select target competitors. Articles, Reddit discussions, and YouTube video transcripts are suitable for analysis.
Step 2: Entity extraction and vectorization
Collect competitors’ content, split it into chunks, and extract entities. Convert it into vector format. Calculate cosine similarity.
Typical result: from 600-700 extracted entities, only about 40 relevant.
Step 3: Defining the Primary and Supporting Entity Network
Divide the entities into two lists:
- Primary Entity — the main entity and its attributes
- Supporting Entity Network — entities located as close as possible to the main one
Example for the query "how to choose a foundation":
| Attribute | Value |
|---|---|
| Foundation type | Strip, pile, slab |
| Materials | Concrete, M300 rebar |
| Characteristics | Water resistance, load-bearing capacity |
Example of triplet relationships:
- Foundation → made of → Concrete
- Foundation → supports → House
- Foundation → depends on → Soil type
- Foundation → requires → Waterproofing
Step 4: Identifying user sub-intents
Each query hides sub-intents (sub-intents). For "how to choose a foundation for a house":
- What types of foundations are there?
- What should be considered when choosing one?
- What load will the foundation bear?
- How does soil type affect it?
- What is the cost of different options?
For the query "best time to play online slots," the sub-intents turned out to be even more interesting:
- Does the time of play affect the return percentage (RTP)?
- When does the casino give bonuses and free spins?
- How does the random number generator (RNG) actually work?
Key insight: "random number generator" is not lexically related to the query "best time to play online slots." But without RNG you will not reach the TOP — Google knows it is a critically important entity.
Step 5: Creating a detailed article outline
An outline is created — a detailed article plan:
- H1-H3 headings with entity distribution
- Summary for each section
- Writing instructions for each paragraph
- Visual elements: infographics, tables, maps
- Entity Connection: how to connect entities
Practical example: how AI creates an outline
Case study — query "how to choose a foundation for a house".
Primary Entity: Foundation
| Attribute | Value |
|---|---|
| Type | Strip, pile, slab, monolithic |
| Material | Concrete, reinforced concrete, FBS blocks |
| Characteristics | Load-bearing capacity, frost resistance, water resistance |
Relationship Triplets:
- Foundation → made of → Concrete
- Foundation → supports → House
- Foundation → depends on → Soil type
- Foundation → requires → Waterproofing
Supporting Entity Network:
- Soil (affects foundation selection)
- House (building load)
- Climate conditions (freezing depth)
- Construction materials
- Groundwater
What the outline looks like for one section:
H3: Climate features — soil freezing depth
Summary: How freezing depth affects the choice of foundation depth.
How to write: Start with a definition. For Moscow — 1.4 m, for Siberia — 2.0–2.4 m.
Visual element: Infographic — a map of soil freezing depth across the regions of Russia.
Entity Connection: “climatic conditions” → “freezing depth” → “foundation depth” → “soil type”. Mention SNiP.
Copywriter Notes: Give values for 5–6 regions. Avoid distant entities (“facade design” is fluff).
How to automate analysis with AI agents
What AI agents can look like
- Code agent in the terminal or VS Code
- Web application — enter the query and region
- Telegram bot — reads channels, creates a summary
- Multi-agent system (SaaS) — several agents working together
Agent workflow logic for SEO content
- Bypasses site protection (Cloudflare, CAPTCHA)
- Extracts competitors' content
- Performs entity extraction
- Vectorizes entities
- Calculates cosine similarity (threshold 0.89+)
- Analyzes relationships
- Identifies sub-intents
- Creates an outline
Result: saves 2–3 hours per article. TOP at the moment of indexing — from 23% to 48%.
Why one piece of AI content ranks in the top, while another does not
There is more AI content than human content now. Google does not ban AI content, but bad content.
Least important factor: keyword inclusion
On a sample of 10 million queries — the least important factor.
Most important factor: Topical Coverage
Intent coverage — topic completeness.
The recipe for the perfect text in 2026
- All intents are covered
- Entities are placed correctly
- Sufficient detail
- No fluff
- Fact-checking has been done — threshold 85%
- Architecture from simple to complex
- Answer to the main intent — above the fold
- The right model — Gemini is not recommended
- Everything important — in the first 1500 words
- Brand mentions on other resources
RAG systems: how to eliminate hallucinations
| Model | Without context | With context |
|---|---|---|
| Claude (Anthropic) | 17% | ~1.3% |
| GPT-4o (OpenAI) | 4% | ~1.3% |
| Gemini (Google) | 38% | ~1.3% |
Optimizing existing pages with AI
How it works
- Provide the URL of your page
- Provide the URLs of competitors from the TOP results
- The agent finds the difference
- Delivers an action plan
Example of an agent recommendation
Task: “Arctic vs Antarctica”
Recommendation 1: Add H2 “What is the difference between tourism in the Arctic and Antarctica” after the section “What is Antarctica”
Recommendation 2: Rewrite the paragraph about climate — it is missing “temperature regime”
Recommendation 3: Create H3 “Tourism Beyond the Arctic Circle”
Priority: up to 0.5 (high)
Top Ranking Factors for 2026
| Factor | Importance | Comment |
|---|---|---|
| Topical Coverage | ⭐⭐⭐⭐⭐ | Most important according to Surfer SEO data |
| Entity Placement | ⭐⭐⭐⭐⭐ | Primary + Supporting Entity Network |
| Lack of “fluff” | ⭐⭐⭐⭐ | Entities far from the topic |
| Fact-checking | ⭐⭐⭐⭐ | 85% threshold |
| Architecture from simple to complex | ⭐⭐⭐⭐ | Top-down logic |
| Intent on the first screen | ⭐⭐⭐⭐ | Above the fold |
| Brand mentions | ⭐⭐⭐⭐ | Positive sentiment |
| First 1500 words | ⭐⭐⭐⭐ | ChatGPT indexing limit |
| Keyword usage | ⭐ | Least important |
Frequently Asked Questions
Does SEO still work in 2026?
Yes, but not in the classical sense. Keywords no longer matter. The focus is now on semantic analysis, entities, and fully covering intents. Sites with semantic SEO get 48% into the TOP versus 23%.
Will Google ban AI content?
No, if the content is high quality. Google fights against low-grade content. The key things are editorial policies, fact-checking, and RAG systems.
How many words does ChatGPT read during indexing?
Approximately the first 1500 words. Place everything important in the first half of the article.
What cosine similarity threshold should be used?
The optimal threshold is 0.89 and above. Out of 600-700 entities, about 40 relevant ones remain.
What is “fluff” from a scientific perspective?
The use of words and entities far from the main topic in the Knowledge Graph. For example, “facade design” in an article about foundations is fluff.
Should keywords be used?
Keyword usage is the least important factor. Focus on entities, intents, and topic completeness.
Which AI models are better to use?
Claude (Anthropic) and GPT-4o (OpenAI) have the fewest hallucinations. Gemini is not recommended.
What matters more: internal or external links?
Both matter. But the most important external factor in 2026 is brand mentions on other resources.
Conclusion
The old SEO formula — keywords + density + matches — no longer works.
The 2026 formula:
- Analyze competitors from the TOP 10
- Extract entities and cosine similarity (0.89+)
- Define the Primary and Supporting Entity Network
- Identify all sub-intents
- Create a detailed outline
- Use editorial policies and fact-checking
- Add brand mentions
Result: a semantically precise article + brand mentions = TOP.
Teams of 150+ SEO specialists have already switched to this approach. Savings — 2–3 hours per article. Top rankings at the moment of indexing — 48% instead of 23%.
Ready to switch to semantic SEO? Start by analyzing one article through an AI agent.
Sources: conference in Chiang Mai (2026), Surfer SEO (10 million queries), Graphite Analytics, HFS, Google Knowledge Graph API, Vectara.