u/reshavkumarfhaman

I had a discussion with my team lead about international SEO strategies. He told me that for a global website, using the lang tag to localize content is better no need for separate subdirectories. He said even if the keywords stay the same, just translating the language is enough. I opposed, and I argued that a directory structure—like /de for Germany, /in for India helps us optimize region-specific keywords and better target each market. What do you think is a lang tag approach sufficient, or is a separate directory structure better for international SEO?

I had a discussion with my SEO manager about whether it's a good practice to use service-page keywords inside blog content. I argued that it's beneficial because of the interlinking between blog posts and service pages, which can help users and improve SEO. But my manager said it’s a bad practice. What’s your take—should service keywords be avoided in blogs, or is interlinking a good enough reason to keep them?

I randomly asked ChatGPT:

“How do you actually pull results from the web?”

I expected a simple answer like:

“we search Google and summarize stuff.”

But the explanation was honestly way more advanced than I thought.

Apparently, when you search something like:

“best SEO services in Mohali”

the system first tries to understand WHAT you actually want.

It figures out things like:

• are you trying to buy something?

• compare options?

• find local businesses?

• just learn information?

So instead of treating it like a normal keyword, it understands the intent behind it.

Then it creates multiple versions of the same search automatically.

Like:

• seo agency mohali

• best seo company mohali

• local seo expert mohali

• digital marketing company mohali

After that, it searches different types of sources at the same time:

• websites

• business listings

• reviews

• Reddit discussions

• Quora answers

• forums

And this part surprised me the most:

It DOESN’T immediately read every website fully.

First it checks:

• titles

• snippets

• relevance

• ranking signals

Then only the best-looking pages get analyzed deeper.

From there, it starts extracting actual useful information like:

• business names

• ratings

• services

• reviews

• locations

• pricing mentions

• authority signals

Then the filtering starts.

It tries to remove:

• fake reviews

• spammy SEO pages

• AI-generated junk

• fake office addresses

• unrealistic promises

It even compares information between sources.

Example:

A company website says:

“10 years experience”

But LinkedIn says the company was created 2 years ago.

That lowers trust automatically.

Then everything gets scored internally based on:

• website quality

• authority

• reviews

• topical expertise

• local relevance

• content quality

And finally the AI summarizes everything into one clean response instead of dumping random links.

So the real workflow is basically:

Query

→ Understand Intent

→ Expand Searches

→ Search Multiple Sources

→ Extract Information

→ Filter Spam

→ Verify Claims

→ Rank Results

→ Generate Final Answer

Honestly, this feels less like a search engine and more like an AI research analyst.

I asked ChatGPT:

“How do you actually show results from the web?”

And it gave me this insane breakdown of the real workflow behind AI web research 👇

Apparently it’s not just:

“search keyword → show links”

The process is way deeper.

First, it detects the INTENT behind your query.

Example:

“best SEO services in Mohali”

The AI classifies it as:

• commercial intent

• local business query

• recommendation-focused search

Then it expands the query into multiple variations like:

• seo agency mohali

• best seo company mohali

• local seo expert mohali

• digital marketing company mohali

After that, it searches multiple sources simultaneously:

• websites

• business listings

• review platforms

• Reddit

• Quora

• forums

Then comes the crazy part…

It doesn’t instantly open every website.

First it fetches:

• titles

• snippets

• ranking signals

• relevance indicators

Only promising pages get analyzed deeper.

From those pages, the system extracts structured data like:

• business names

• ratings

• reviews

• services

• locations

• pricing mentions

• authority signals

Then it starts filtering spam.

Things like:

• fake reviews

• keyword stuffing

• AI-generated junk pages

• fake office locations

• unrealistic SEO guarantees

ALL get detected.

It even cross-verifies information across sources.

Example:

A website says:

“10 years experience”

But LinkedIn says:

Company created 2 years ago.

Trust score decreases.

Then every result gets internally scored using factors like:

• website quality

• reviews

• topical expertise

• authority

• local relevance

• content quality

And finally…

The AI summarizes everything into a clean answer instead of dumping raw links.

So the actual workflow is basically:

User Query

→ Intent Detection

→ Query Expansion

→ Multi-source Search

→ Content Extraction

→ Spam Filtering

→ Verification

→ Ranking

→ AI Summary

→ Final Answer

Honestly… this felt less like “search” and more like a full research engine.