How LLMs Crawl and Index the Web Differently from Google

Intro

Google has spent 25 years perfecting one core system:

crawl → index → rank → serve

But modern AI search engines — ChatGPT Search, Perplexity, Gemini, Copilot — operate on an entirely different architecture:

crawl → embed → retrieve → synthesize

These systems are not search engines in the classical sense. They don’t rank documents. They don’t evaluate keywords. They don’t compute PageRank.

Instead, LLMs compress the web into meaning, store those meanings as vectors, and then reconstruct answers based on:

• semantic understanding

• consensus signals

• trust patterns

• retrieval scoring

• contextual reasoning

• entity clarity

• provenance

This means marketers must fundamentally rethink how they structure content, define entities, and build authority.

This guide breaks down how LLMs “crawl” the web, how they “index” it, and why their process is nothing like Google’s traditional search pipeline.

1. Google’s Pipeline vs. LLM Pipelines

Let’s compare the two systems in the simplest possible terms.

Google Pipeline (Traditional Search)

Google follows a predictable four-step architecture:

1. Crawl

Googlebot fetches pages.

2. Index

Google parses text, stores tokens, extracts keywords, applies scoring signals.

3. Rank

Algorithms (PageRank, BERT, Rater Guidelines, etc.) determine which URLs appear.

4. Serve

User sees a ranked list of URLs.

This system is URL-first, document-first, and keyword-first.

LLM Pipeline (AI Search + Model Reasoning)

LLMs use a completely different stack:

1. Crawl

AI agents fetch content from the open web and high-trust sources.

2. Embed

Content is transformed into vector embeddings (dense meaning representations).

3. Retrieve

When a query arrives, a semantic search system pulls the best matching vectors, not URLs.

4. Synthesize

The LLM merges information into a narrative answer, optionally citing sources.

This system is meaning-first, entity-first, and context-first.

In LLM-driven search, relevance is calculated through relationships, not rankings.

2. How LLM Crawling Actually Works (Not Like Google at All)

LLM systems don’t operate one monolithic crawler. They use hybrid crawling layers:

Layer 1 — Training Data Crawl (Massive, Slow, Foundational)

This includes:

• Common Crawl

• Wikipedia

• government datasets

• reference materials

• books

• news archives

• high-authority sites

• Q&A sites

• academic sources

• licensed content

This crawl takes months — sometimes years — and produces the foundation model.

You cannot “SEO” your way into this crawl. You influence it through:

• backlinks from authoritative sites

• strong entity definitions

• widespread mentions

• consistent descriptions

This is where entity embeddings first form.

Layer 2 — Real-Time Retrieval Crawlers (Fast, Frequent, Narrow)

ChatGPT Search, Perplexity, and Gemini have live crawling layers:

• real-time fetchers

• on-demand bots

• fresh content detectors

• canonical URL resolvers

• citation crawlers

These behave differently than Googlebot:

• ✔ They fetch far fewer pages

• ✔ They prioritize trusted sources

• ✔ They parse only key sections

• ✔ They build semantic summaries, not keyword indexes

• ✔ They store embeddings, not tokens

A page doesn’t need to “rank” — it just needs to be easy for the model to extract meaning from.

Layer 3 — RAG (Retrieval-Augmented Generation) Pipelines

Many AI search engines use RAG systems that operate like mini-search engines:

• they build their own embeddings

• they maintain their own semantic indexes

• they check content freshness

• they prefer structured summaries

• they score documents based on AI suitability

This layer is machine-readable first — structure matters more than keywords.

Layer 4 — Internal Model Crawling (“Soft Crawling”)

Even when LLMs aren’t crawling the web, they “crawl” their own knowledge:

• embeddings

• clusters

• entity graphs

• consensus patterns

When you publish content, LLMs evaluate:

• does this reinforce existing knowledge?

• does it contradict consensus?

• does it clarify ambiguous entities?

• does it improve factual confidence?

This soft crawl is where LLMO matters most.

3. How LLMs “Index” the Web (Completely Different from Google)

Google’s index stores:

• tokens

• keywords

• inverted indexes

• page metadata

• link graphs

• freshness signals

LLMs store:

• ✔ vectors (dense meaning)

• ✔ semantic clusters

• ✔ entity relationships

• ✔ concept maps

• ✔ consensus representations

• ✔ factual probability weights

• ✔ provenance signals

This difference cannot be overstated:

**Google indexes documents.

LLMs index meaning.**

You don’t optimize for indexing — you optimize for understanding.

4. The Six Stages of LLM “Indexing”

When an LLM ingests your page, this is what happens:

Stage 1 — Chunking

Your page is split into meaning blocks (not paragraphs).

Well-structured content = predictable chunks.

Stage 2 — Embedding

Each chunk is converted into a vector — a mathematical representation of meaning.

Weak or unclear writing = noisy embeddings.

Stage 3 — Entity Extraction

LLMs identify entities like:

• Ranktracker

• keyword research

• backlink analysis

• AIO

• SEO tools

• competitor names

If your entities are unstable → indexing fails.

Stage 4 — Semantic Linking

LLMs connect your content with:

• related concepts

• related brands

• cluster topics

• canonical definitions

Weak clusters = weak semantic linking.

Stage 5 — Consensus Alignment

LLMs compare your facts with:

• Wikipedia

• government sources

• high-authority sites

• established definitions

Contradictions = penalty.

Stage 6 — Confidence Scoring

LLMs assign probability weights to your content:

• How trustworthy is it?

• How consistent?

• How original?

• How aligned with authoritative sources?

• How stable over time?

These scores determine whether you are used in generative answers.

5. Why LLM “Indexing” Makes SEO Tactics Obsolete

A few major consequences:

• ❌ Keywords don’t determine relevance.

Relevance comes from semantic meaning, not matching strings.

• ❌ Links matter differently.

Backlinks strengthen entity stability and consensus, not PageRank.

• ❌ Thin content is ignored instantly.

If it can’t build stable embeddings → it's useless.

• ❌ Duplicate content destroys trust.

LLMs downweight repeated patterns and non-original text.

• ❌ E-A-T evolves into provenance.

It’s not about “expertise signals” anymore — it’s about traceable authenticity and trustworthiness.

• ❌ Content farms collapse.

LLMs suppress low-originality, low-provenance pages.

• ❌ Ranking doesn’t exist — citation does.

Visibility = being chosen during synthesis.

6. What LLMs Prefer in Web Content (The New Ranking Factors)

The top traits LLMs prioritize:

• ✔ clear definitions

• ✔ stable entities

• ✔ structured content

• ✔ consensus alignment

• ✔ strong topical depth

• ✔ schema

• ✔ original insights

• ✔ author attribution

• ✔ low ambiguity

• ✔ consistent clusters

• ✔ high authority sources

• ✔ reproducible facts

• ✔ logical formatting

If your content meets all of these → it becomes “LLM-preferred.”

If not → it becomes invisible.

7. Practical Differences Marketers Must Adapt To

**Google rewards keywords.

LLMs reward clarity.**

**Google rewards backlinks.

LLMs reward consensus.**

**Google rewards relevance.

LLMs reward semantic authority.**

**Google ranks documents.

LLMs choose information.**

**Google indexes pages.

LLMs embed meaning.**

These are not small differences. They require rebuilding the entire content strategy.

Final Thought:

You’re Not Optimizing for a Crawler — You’re Optimizing for an Intelligence System

Googlebot is a collector. LLMs are interpreters.

Google stores data. LLMs store meaning.

Google ranks URLs. LLMs reason with knowledge.

This shift demands a new approach — one built on:

• entity stability

• canonical definitions

• structured content

• semantic clusters

• cross-source consensus

• provenance

• trustworthiness

• clarity

This is not SEO evolution — it is search system replacement.

If you want visibility in 2025 and beyond, you must optimize for how AI sees the web, not how Google sees the web.