To effectively optimize for generative engines, you must first understand the landscape – the platforms involved, how they function, and how user behavior is shifting. GEO (Generative Engine Optimization) parallels traditional SEO in goal (visibility) but targets a new class of “answer engines” rather than classic search engines.
Conventional search engines like Google and Bing return a ranked list of results (web pages) for users to click. Generative engines, by contrast, produce a direct answer or overview synthesizing information from many sources. They often use large language models (LLMs) combined with search indexes – retrieving relevant documents and then generating a cohesive response. For example, Bing Chat takes your query, performs a web search, and then the LLM writes an answer citing the top sources. Google’s SGE uses its LLM (Gemini) to create an “AI snapshot” answer at the top of the results, with key points and links to supporting websites. Meanwhile, standalone QA bots like ChatGPT or Anthropic’s Claude rely partly on their trained knowledge (up to a certain date) and partly on retrieval (if browsing is enabled or via plugins).
Key platforms in the generative search landscape include:
- ChatGPT (OpenAI): The most famous LLM chatbot, now with browsing (via plugins or built-in for some versions) which allows it to fetch live web content. By default, ChatGPT’s knowledge cutoff is 2021, but with access to the web it behaves like a generative engine. It doesn’t automatically cite sources unless explicitly asked, so from a GEO perspective, getting mentioned by ChatGPT may not always mean attribution – but it means your info was influential. There are also domain-specific instances (e.g., Bing Chat uses GPT-4 with Bing search).
- Google SGE / Gemini: Google’s Search Generative Experience is an AI-enhanced search integrated into Google’s results. It’s experimental (as of 2024) but expected to roll into mainstream search soon. SGE appears as a colored box with an AI-generated summary and follow-up questions, often accompanied by a handful of source links or cards. Google’s underlying model for these AI overviews is Gemini (successor to PaLM), and it’s tightly integrated with Google’s Knowledge Graph and index. Notably, Google ensures citations are present for verification. For marketers, Google SGE is critical – it’s projected to cover a large portion of queries (some reports show SGE appearing on 64%+ of SERPs in tests).
- Bing AI (Bing Chat/Copilot): Microsoft’s Bing was the first major search engine to integrate an LLM (GPT-4). Bing Chat provides detailed answers with footnote numbers that link to sources. It often pulls content from the top 3-5 search results, but not exclusively the very top results. Bing also integrated its chat into products like Windows (Copilot in Windows 11 and Edge sidebar). This means your content might be delivered to users through Bing in many contexts. Ensuring you rank in Bing and have content worthy of being quoted increases chances of being featured. Bing’s usage surged after adding AI; it poses new competition to Google’s dominance.
- Perplexity.ai: An AI search engine that is explicitly built to answer questions by retrieving web results and citing them. Perplexity presents a conversational answer with footnotes and even a list of sources consulted. For GEO, Perplexity is interesting because it rewards very clear, succinct answers – it often quotes definitions or concise explanations. If your site provides a great direct answer to a question, Perplexity might quote it and list your URL. Moreover, Perplexity usage has grown (10M+ monthly users, high growth rate).
- Others: There are other players like You.com’s Chat (which also cites sources), DuckDuckGo’s DuckAssist (an AI answer pulling from Wikipedia mainly), and NeevaAI (now closed, but it was an early AI search). Additionally, voice assistants (Alexa, Siri) are leaning into generative AI for more natural responses, which could tie into GEO in the future (e.g., an AI voice assistant citing your site’s info). We also see domain-specific generative engines (for programming, StackExchange has experimented with an AI summary of answers, etc.). Keep an eye on large platforms like Meta (Facebook/Instagram) – their AI assistants or search within social networks might start surfacing external info with generative summaries.
The common thread is that these engines aim to answer user queries directly and fast. They use web content as fuel but intermediate the experience, which introduces both opportunity and risk for content creators.