Search is undergoing a profound shift: from classic SEO built on “10 blue links” to GEO, where LLM-powered engines (ChatGPT with search, Google SGE/Gemini, Perplexity) deliver direct, cited answers. Queries are longer and more conversational, and systems remember context, reason across sources, and synthesize according to user intent. In this new game, visibility is no longer “being on page one,” but being part of the answer.
Who’s leading
Google SGE: Generates a “snapshot” with synthesis and citations, integrates the Shopping Graph, and avoids sensitive YMYL topics.
ChatGPT with search: Decides when to query the web, cites sources, and blends real-time data in a conversational UI.
Perplexity: Applies RAG—“always retrieve before you generate”—with concise answers, footnotes, and domain filtering.
Bing/Copilot and alternatives (You.com, Komo, Andi) explore privacy, accuracy, and specialized UIs.
From SEO to GEO: how to optimize content to be cited by AI
Structure and semantics: Clear text with headings, lists, FAQs, and summaries (“TL;DR,” “In summary”) that an LLM can reuse.
Data and references: Adding statistics, dates, expert quotes, and citing sources increases inclusion likelihood.
Tone and concision: An expert voice and precise paragraphs; LLMs prioritize information-dense fragments.
Schema.org and structured data: FAQ/HowTo, well-formed lists, and clean HTML aid extraction.
Intent coverage: Informational content, comparisons (“X vs. Y,” “Top 10”), transactional pages, and up-to-date “About” pages.
UGC and communities: Useful presence in forums (e.g., Reddit) influences how AI mentions brands and solutions.
Monitor “reference rate”: Track how often AI cites or mentions your brand and identify content gaps.
KPIs for the GEO era
For publishers/marketers
Impressions in AI answers, clicks, and AI CTR (Search Console already surfaces SGE signals).
Brand mentions and sentiment in generic answers (“share of AI voice”).
Traffic and conversions from AI referrals; compare behavior versus traditional organic.
For engine builders
Factual accuracy and hallucinations; source diversity and sub-question coverage.
Engagement: Conversational session length, follow-up rate, and ratings.
Efficiency: Latency, cost per query, and precision in deciding when to search.
Technical best practices for LLM-based engines
RAG architecture: Separate retrieval (indexes/APIs, hybrid keyword+vector) from generation.
Prompts with citations and a cap on relevant snippets; verify source→claim alignment.
Guardrails: Moderation, abstain when evidence is insufficient, YMYL policies.
Continuous improvement: Human/synthetic tests, A/B on prompts and retrieval, feedback loops.
Scale and performance: Caches, token reduction, parallelization, and model choice aligned to SLAs.
Conclusion
GEO inaugurates search’s “Act II”: from links to language. Those who win will produce structured, evidence-backed, easily citable content and measure their presence in generative answers—not just SERPs. For builders, the combination of RAG + guardrails + rigorous evaluation is essential. The market is being reshaped, and rules evolve with every model update: adapt quickly and think, “Will the model remember my brand?”
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Lorenzo, congratulations — it's a very interesting research. We are in the early stages of the evolution from SEO to GEO, and although the evolution is happening very quickly, there is still a long way to go: defining new KPIs, identifying success factors, etc. I encourage you to keep researching.