Can someone explain how AI search engines actually work? They seem fundamentally different from Google

Let me break down the fundamental differences:

Traditional Search (Google) vs AI Search:

The three core components of AI search:

1. Large Language Model (LLM) The “brain” trained on massive text data. Understands language patterns and can generate coherent responses. But has a knowledge cutoff date.

2. Retrieval-Augmented Generation (RAG) Solves the knowledge cutoff problem. Retrieves current information from the web in real-time, then feeds it to the LLM.

3. Embedding Models Converts text into numerical vectors that capture meaning. Enables semantic search - finding relevant content even without exact keyword matches.

The process when you query:

1. Your query is converted to a vector
2. System searches for semantically similar content
3. Retrieved content is passed to the LLM
4. LLM generates answer using retrieved context
5. Citations link back to sources

Let me add the platform-specific breakdown:

How different AI search platforms work:

ChatGPT:

• 81% market share, 2 billion daily queries
• Uses ChatGPT-User crawler for real-time web access
• Training data + RAG hybrid
• Prefers authoritative sources (Wikipedia, major publications)

Perplexity:

• Real-time web search focused
• Shows sources explicitly in response
• Cites diverse sources (Reddit, YouTube, industry sites)
• Transparency-first approach

Google AI Overviews:

• 18% of Google searches show AI Overviews
• Uses Google’s existing index + Gemini
• Integrates with traditional search results
• 88% of triggering queries are informational

Google AI Mode:

• Separate experience, restructured around AI
• 100 million monthly users
• Prefers brand/OEM websites (15.2% of citations)

Key insight: Each platform has different source preferences. Optimizing for all requires understanding these differences.

Let me explain semantic search since it’s core to understanding AI search:

Traditional keyword search: Query: “affordable smartphones good cameras” Matches: Pages containing those exact words

Semantic search: Query: “affordable smartphones good cameras” Understands: User wants budget phones with excellent camera capabilities Matches: Content about “budget phones with great photography features” (no exact keyword match needed)

How this works technically:

Vector embeddings: Text is converted to high-dimensional numerical arrays. Semantically similar content = similar vectors.

“King” and “Queen” would have similar vectors “King” and “Refrigerator” would have very different vectors

Cosine similarity: System measures the “distance” between query vector and content vectors. Closer = more relevant.

Why this matters for optimization:

• Keywords matter less than semantic coverage
• Topic authority beats keyword density
• Related concepts strengthen relevance
• Natural language beats keyword stuffing

Practical implication: Write naturally about your topic, covering related concepts thoroughly. AI will find you for queries you never explicitly targeted.

This is incredibly helpful. The semantic search explanation especially clarifies why our keyword-focused content sometimes doesn’t appear while our comprehensive guides do.

Question: You mentioned RAG retrieves content in real-time. Does that mean our content needs to be fresh to be retrieved? Or does it use older content too?

Great question on freshness:

RAG and content freshness:

RAG can retrieve both new and old content, but there are preferences:

Recency signals matter:

• ~50% of citations come from content in last 11 months
• Only ~4% from content published in the last week
• Time-sensitive topics heavily favor recent content
• Evergreen topics balance recency with authority

The ideal scenario: Authoritative content that’s regularly updated. “Evergreen + Fresh” beats both purely new content and old stale content.

Platform differences:

• Perplexity: More real-time, favors recent content
• ChatGPT: Balances training data + real-time retrieval
• Google AI: Uses existing index freshness signals

Optimization strategy:

1. Create comprehensive, authoritative base content
2. Update regularly with fresh data points
3. Use dateModified schema to signal updates
4. Add new sections rather than just republishing

The “last updated” signal is increasingly important. AI systems can see when content was actually modified, not just republished.

Let me go deeper on RAG since it’s central to AI search:

The RAG process step-by-step:

1. Query processing - Your question is analyzed for intent and key concepts

2. Query expansion - System generates multiple related subqueries to improve retrieval

3. Vector search - Queries converted to vectors, matched against indexed content

4. Document retrieval - Top matching content chunks are retrieved

5. Passage extraction - Most relevant passages extracted (not whole documents)

6. Context assembly - Retrieved passages organized for the LLM

7. Response generation - LLM generates answer using retrieved context

8. Citation attachment - Sources that contributed to the answer are cited

Why chunking matters: Content is typically chunked into 200-500 word segments. If your key information spans chunk boundaries, it may not be retrieved together.

Optimization based on RAG:

• Make each section self-contained
• Lead with key information
• Use clear headers as chunk boundaries
• Ensure important facts aren’t buried mid-paragraph

Understanding RAG explains why structure matters so much for AI search.

From a brand perspective, here’s what’s different about AI search:

The visibility paradigm shift:

Traditional search:

• Compete for 10 positions on page 1
• Ranking = visibility

AI search:

• Content either cited or not
• Multiple sources can be cited
• Citations happen for specific queries, not globally
• Brand mention in response = visibility

Statistics that matter:

• AI search traffic converts at 14.2% vs Google’s 2.8%
• 40% of AI-cited sources rank outside Google’s top 10
• Branded mentions correlate 0.664 with AI Overviews (higher than backlinks at 0.218)

What this means:

• Traditional rankings don’t guarantee AI visibility
• Brand authority matters more than domain authority
• Being mentioned beats being ranked
• AI search traffic is more valuable per visit

The opportunity: Sites that don’t rank well in traditional search can still get AI citations. The playing field is different - it’s about being the best answer, not the best-optimized page.

The conversion rate difference is striking - 14.2% vs 2.8%. And the low correlation between backlinks and AI visibility suggests our traditional link building investments may not translate.

How do we track our AI search performance? With Google, we have Search Console. What’s the equivalent for AI search?

Unfortunately, there’s no equivalent to Search Console for AI search yet. But here’s what we do:

Monitoring approaches:

1. Dedicated tools - Am I Cited tracks brand/URL mentions across AI platforms. Shows which queries trigger your citations, competitor comparison, trends over time.

2. Manual testing - Regular testing of target queries across platforms. Document which answers cite you and which don’t.

3. Log analysis - Track AI crawler visits and correlate with citation appearances.

4. Referral traffic - Monitor referrals from AI platforms in analytics (though attribution is tricky).

Key metrics to track:

• Citation frequency (how often you’re cited)
• Citation share of voice (you vs. competitors)
• Query coverage (which topics cite you)
• Platform distribution (ChatGPT vs. Perplexity vs. Gemini)

What Am I Cited shows us:

• Queries where we’re cited vs. not
• Which competitors appear when we don’t
• Citation trends over time
• Content that drives the most citations

Without this monitoring, you’re optimizing blind. The feedback loop is essential.

Some forward-looking context on where AI search is heading:

Growth trajectory:

• AI search traffic up 357% year-over-year
• ChatGPT: 700 million weekly active users (4x YoY)
• Google AI Mode: 100 million monthly users
• Prediction: AI search traffic to surpass traditional by 2028

Emerging capabilities:

• ChatGPT Agent Mode: Users can delegate tasks (book flights, make purchases)
• ChatGPT Instant Checkout: Buy products directly in chat
• Voice and multimodal search increasing
• Real-time integration becoming standard

Strategic implications:

• AI isn’t just an alternative search channel - it’s becoming a commerce platform
• Being cited in AI isn’t just visibility - it can drive direct transactions
• The stakes are higher than traditional search because AI often “completes” the user journey

Bottom line: Understanding AI search isn’t optional anymore. It’s rapidly becoming the primary way consumers discover and make decisions.

Incredible thread. Here’s my synthesis:

How AI search works:

• LLM (the brain) + RAG (real-time retrieval) + semantic search (meaning-based matching)
• Generates synthesized answers with citations
• Very different from Google’s ranked links model

Key differences from traditional SEO:

• Semantic relevance > keyword matching
• Brand mentions > backlinks for AI visibility
• Content structure matters for RAG retrieval
• Multiple sources can be cited (not just top 10)

Higher stakes:

• 14.2% conversion rate vs. Google’s 2.8%
• AI search growing rapidly (357% YoY)
• Becoming a commerce platform, not just search

Monitoring:

• No Search Console equivalent yet
• Tools like Am I Cited track citations
• Need active monitoring, not just ranking tracking

This fundamentally changes our strategy. Time to shift resources.