Come funziona l'indicizzazione per la ricerca AI? È diversa dall'indicizzazione di Google?

Let me explain the technical architecture.

Two mechanisms for AI content access:

1. Training Data (Historical)

How it works:

• Models are trained on web snapshots from Common Crawl, books, etc.
• Content is processed, tokenized, embedded in model weights
• Knowledge is “baked in” at training time
• Knowledge cutoff date applies

Implications:

• Content from before training cutoff may influence responses
• You can’t “update” training data once model is trained
• Historical authority matters

2. RAG Retrieval (Real-time)

How it works:

• User query triggers search across knowledge base
• Relevant documents retrieved (often from web search)
• Retrieved content added to prompt context
• Model generates response using retrieved content

Technical flow:

Query → Embedding → Vector Search →
Document Retrieval → Re-ranking →
Context Augmentation → Generation → Response

Implications:

• Current content can be cited
• Retrieval depends on search quality and accessibility
• Your content must be retrievable by AI systems

The key difference from Google:

Google: Crawl → Index → Rank pages → Display links RAG: Query → Search → Retrieve passages → Synthesize answer

AI retrieves and synthesizes. Google ranks and links.

Adding technical depth on the retrieval process.

How RAG retrieval actually works:

Step 1: Query Processing

"What is the best CRM for small business?"
↓
Tokenize → Embed → Query Vector

Step 2: Vector Search

Query Vector compared to document vectors
Semantic similarity scoring
Top-K relevant documents retrieved

Step 3: Re-ranking

Initial results re-scored
Authority signals considered
Freshness weighted
Final ranking produced

Step 4: Context Augmentation

Retrieved passages added to prompt
Source metadata preserved
Token limits managed

What affects your retrieval:

1. Semantic relevance - Does your content semantically match queries?
2. Content structure - Can passages be cleanly extracted?
3. Authority signals - Is your domain trusted?
4. Freshness - How recently was content updated?
5. Accessibility - Can the system actually fetch your content?

The indexing difference:

Google: Page-level ranking with hundreds of signals RAG: Passage-level retrieval with semantic matching

Your page might rank #1 on Google but not be retrieved by RAG if:

• Content isn’t semantically matching queries
• Passages aren’t cleanly extractable
• Technical barriers prevent access

Technical implementation perspective.

Ensuring AI systems can access your content:

Robots.txt:

User-agent: GPTBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Google-Extended
Allow: /

Server-side rendering:

AI crawlers typically don’t execute JavaScript well. If your content loads via JS:

• Use SSR (Next.js, Nuxt, etc.)
• Pre-render pages
• Ensure critical content in initial HTML

Response time:

AI crawlers are less patient than Google. Optimize for:

• TTFB < 200ms
• Full page load < 2 seconds
• No aggressive rate limiting on bots

Structured data:

Helps AI systems understand content:

{
  "@type": "Article",
  "headline": "...",
  "author": { ... },
  "datePublished": "...",
  "dateModified": "..."
}

The verification:

Check server logs for AI crawler activity:

• GPTBot
• ClaudeBot
• PerplexityBot

If you’re not seeing crawl requests, something’s blocking them.

How content structure affects AI retrieval.

The passage extraction reality:

AI systems don’t read whole pages. They extract passages that answer queries. Your content structure determines what gets extracted.

Good for extraction:

## What is GEO?

GEO (Generative Engine Optimization) is the practice
of optimizing content to be cited in AI-generated
responses. It focuses on earning citations rather
than rankings.

Clean passage, easy to extract and cite.

Bad for extraction:

## The Evolution of Digital Marketing

In recent years, as technology has advanced, we've
seen many changes in how businesses approach online
visibility. One emerging area, sometimes called GEO
or generative engine optimization, represents a shift
in thinking about how content gets discovered...

Buried answer, hard to extract.

Technical structure recommendations:

• H2s as questions matching user queries
• First paragraph as direct answer
• Subsequent paragraphs as supporting detail
• Lists and tables for structured information
• Clear semantic HTML structure

Schema for passages:

Consider marking up FAQs with schema - explicit question/answer structure that AI can parse:

{
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "What is GEO?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "GEO is..."
    }
  }]
}

Performance factors for AI crawling.

What I’ve learned from log analysis:

AI crawler behavior:

• Less patient than Googlebot
• Abandon slow pages faster
• Retry less often on failures
• Respect rate limits strictly

The numbers that matter:

Recommendations:

1. CDN with edge caching for AI crawlers
2. Bot-specific rate limits that don’t throttle AI crawlers
3. Pre-rendered pages for critical content
4. Monitoring of AI crawler success rates

The infrastructure play:

If AI crawlers can’t reliably access your content, you won’t be in their retrieval pool, period.

Bridging Google indexing and AI retrieval.

Google indexing helps AI because:

1. ChatGPT uses Bing (significant overlap with Google)
2. Perplexity references authoritative sources (Google often surfaces these)
3. Google AI Overview directly uses Google’s index

But Google indexing isn’t sufficient because:

1. AI crawlers are separate from Googlebot
2. Content structure for ranking ≠ structure for extraction
3. Technical requirements differ
4. AI retrieval is passage-level, not page-level

The technical checklist:

For Google (traditional):

• Crawlable by Googlebot
• Proper canonicals
• Internal linking
• Page-level optimization

For AI retrieval (additional):

• AI crawlers allowed
• Server-side rendering
• Passage-level structure
• Fast, reliable serving
• Semantic content matching

Do both.

Google indexing is necessary but not sufficient for AI visibility.

This thread clarified the technical landscape.

My key takeaways:

Two AI content mechanisms:

1. Training data (historical, baked-in)
2. RAG retrieval (real-time, per-query)

RAG retrieval process:

• Query embedding → Vector search → Document retrieval → Re-ranking → Synthesis

Key differences from Google:

• Passage-level not page-level
• Semantic matching not keyword matching
• Extraction quality matters

Technical requirements:

• AI crawlers allowed in robots.txt
• Server-side rendering essential
• Fast response times (<200ms TTFB)
• Clean content structure for extraction

Action items:

1. Audit robots.txt for AI crawler access
2. Verify SSR implementation
3. Check server logs for AI crawler activity
4. Structure content for passage extraction
5. Implement comprehensive schema

Thanks for the technical depth!