AI Search & SEO: How AI Search Engines and LLMs Discover Information

The landscape of search has fundamentally transformed. While millions were optimizing for Google's traditional blue links, a revolution quietly unfolded. Today, over 700 million people use ChatGPT weekly, Perplexity processes billions of queries monthly, and Google's AI Overviews now appear on more than 55% of search results pages globally. According to recent data, AI-referred sessions jumped an astounding 527% between January and May 2025, signaling a seismic shift in how people discover information.

The question is no longer whether AI search will impact your business—it's whether you're ready to adapt before your competitors do. This comprehensive guide reveals everything you need to know about AI search engines in 2025, how large language models (LLMs) retrieve and process information, and the emerging field of Generative Engine Optimization (GEO) that's reshaping SEO as we know it.

What Are AI Search Engines?

AI search engines represent a fundamental shift from traditional search technology. Rather than simply displaying a list of links ranked by relevance, AI search engines use artificial intelligence—specifically large language models (LLMs)—to synthesize information from multiple sources and deliver summarized, conversational responses directly to users.

The Core Difference

Traditional search engines work as giant indexes, cataloging web pages and matching them to keyword queries. They excel at fetching information that already exists on the internet and presenting it in structured ways—blue links, featured snippets, knowledge panels, maps, and videos. The user still does the heavy lifting of clicking through results, reading multiple pages, and synthesizing information themselves.

AI search engines, by contrast, do this synthesis work for users. They don't just fetch—they understand, interpret, and generate. Using advanced natural language processing (NLP) and machine learning, they grasp the semantic context and intent behind queries, allowing them to handle complex, conversational questions that traditional keyword-based systems struggle with.

As one industry expert put it: "Search is no longer just about typing keywords and scrolling through endless links. In 2025, AI search engines are changing how we discover, analyze, and verify information." Instead of viewing search as a one-off transaction, AI repositions it as an ongoing interaction where each query builds upon the last, allowing the engine to adapt its understanding and deliver increasingly relevant information.

How AI Search Engines Work

Understanding the mechanics behind AI search engines requires examining three foundational technologies that work in concert: large language models, retrieval-augmented generation, and semantic search capabilities.

Large Language Models (LLMs)

At the heart of AI search engines are large language models—neural networks trained on vast amounts of text data that can understand and generate human-like text. LLMs like GPT-4, Claude, and Gemini contain billions to trillions of parameters and operate as general-purpose sequence models capable of generating, summarizing, translating, and reasoning over text.

These models work as sophisticated statistical prediction machines. They learn patterns in text during training and use those patterns to predict what comes next in a sequence. When you ask a question, the LLM processes it by breaking it down into tokens (pieces of words) and using its learned patterns to generate a relevant response.

However, LLMs face a critical limitation: their knowledge is frozen at the time of training. A model trained in early 2025 won't know about events that happened later that year. This is where retrieval-augmented generation comes in.

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is the breakthrough technology that transforms static language models into dynamic search engines. RAG works by combining LLMs with real-time information retrieval systems, allowing the model to pull in current, relevant data before generating responses.

Here's how the RAG process works in practice:

Query Processing: When you submit a search query, the system first analyzes and potentially rewrites it to better align with retrieval requirements. Research shows that AI can rewrite queries to improve search effectiveness, though this must be balanced carefully to avoid "concept drift"—introducing unrelated information that skews results.
Document Retrieval: The system searches through external knowledge bases, databases, or the web to find relevant documents. This typically involves encoding both the query and documents as vectors (numerical representations) and finding the closest matches in a vector database. Leading AI search engines can issue hundreds of parallel searches to gather comprehensive information.
Context Augmentation: The retrieved documents are added to the LLM's context window—essentially its working memory. Modern LLMs can handle context windows of hundreds of thousands of tokens, allowing them to work with extensive source material.
Response Generation: The LLM generates a response based on both its training knowledge and the retrieved documents, typically citing sources to increase transparency and trust.

This architecture allows AI search engines to provide up-to-date information without requiring constant retraining of the underlying model. As AWS explains, "RAG extends the already powerful capabilities of LLMs to specific domains or an organization's internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts."

Semantic Search and Natural Language Processing

AI search engines leverage semantic search capabilities that go far beyond simple keyword matching. Using natural language processing, these systems understand the meaning and context behind queries, not just the literal words used.

For instance, if you search for "how to fix a leaky faucet," a traditional search engine matches those keywords to pages. An AI search engine understands you're looking for repair instructions, recognizes "leaky faucet" as a plumbing issue, and can even infer that you might need information about tools required, common causes, and when to call a professional—all without you explicitly asking.

This contextual understanding extends to handling conversational queries, following up on previous questions, and adapting to user preferences. As NLP continues advancing in 2025, these systems are becoming even better at understanding nuanced language, including slang, idioms, and local dialects.

The Query Fan-Out Technique

One of the most powerful techniques used by modern AI search engines is called "query fan-out." When you ask a complex question, the system breaks it down into multiple sub-queries and executes them in parallel. For example, Google's Deep Search feature can issue hundreds of searches, reason across disparate pieces of information, and create comprehensive, fully-cited reports in just minutes.

This approach allows AI search engines to tackle questions that would require hours of manual research. According to reports from early 2025, professionals are cutting their research time by 20-30% using these capabilities, with some researchers reporting their literature review time was cut in half.

Examples of AI-Powered Search Engines

The AI search landscape in 2025 is diverse and rapidly evolving. Here are the major players reshaping how people discover information:

ChatGPT Search (OpenAI)

ChatGPT has become ubiquitous in the AI space, with over 700 million weekly active users as of 2025. While initially a pure chatbot, ChatGPT now includes powerful search capabilities that combine OpenAI's language models with real-time web access. The platform is particularly strong for creative tasks, coding assistance, and complex problem-solving.

ChatGPT's search feature averages about 10.42 citations per response and has introduced "Deep Research" capabilities for in-depth investigations. However, access is limited—free users have restricted access, and even Plus subscribers are capped at 30 deep searches per month. Recent studies show ChatGPT excels in versatility but sometimes includes outdated sources compared to competitors.

Perplexity AI

Perplexity has positioned itself as an "answer engine" specifically designed for research and information discovery. With 780 million monthly queries in 2025 (up from just 230 million a year prior), it's experiencing explosive growth. The platform's tagline "Skip the links" encapsulates its value proposition.

Key advantages of Perplexity include:

Inline citations: Every claim is linked directly to its source, making verification easy
Speed: Consistently faster response times than competitors, typically 1-2 seconds even for complex queries
Accuracy: Studies show error rates around 13%, compared to 26% for some Google AI Overview responses on technical topics
Research tools: Specialized features for academic papers, SEC filings, and other authoritative sources
Pro Search: Deep research capabilities with customizable agents for specific domains

Perplexity shines particularly in scenarios requiring citation-rich, verifiable answers. Multiple comparative tests show it excels at synthesizing information from multiple authoritative sources and presenting it clearly with proper attribution.

Google AI Overviews and AI Mode

Google, the dominant player in traditional search, has integrated AI throughout its platform. AI Overviews now appear on approximately 47% of search results globally (with some studies showing up to 55%), particularly for informational and how-to queries. In major markets like the US and India, AI Overviews drive over 10% increases in search usage for queries where they appear.

In May 2025, Google announced AI Mode—a more advanced search experience that goes beyond simple overviews. AI Mode features include:

Multimodal search: Integration with Google Lens allows users to search using their camera in real-time
Interactive visualizations: Automatically generated graphs and charts for comparative queries
Deep Search: Similar to ChatGPT, issuing hundreds of parallel searches for comprehensive reports
Agentic capabilities: Integration with Project Mariner for tasks like booking tickets and making reservations
Speed: Industry-leading response times of 0.3-0.6 seconds, leveraging Google's massive infrastructure

Google's AI features benefit from seamless integration with Maps, Calendar, Shopping, and other Google services, making it particularly strong for local searches, shopping queries, and practical everyday tasks.

Claude (Anthropic)

Anthropic's Claude has gained recognition for its thoughtful, nuanced responses and strong performance on reasoning tasks. While not traditionally marketed as a search engine, Claude includes web search capabilities and is increasingly used for research and information discovery. Claude is particularly noted for:

Extended context windows: Ability to process and analyze extremely long documents
Careful, measured responses: Less prone to overconfident claims
Strong citation practices: Transparent sourcing in research tasks

Microsoft Copilot (Bing AI)

Microsoft's Copilot, powered by OpenAI's GPT models and integrated into Bing, takes a minimalist approach with concise, actionable responses. Copilot typically provides fewer references than competitors (often 3-5 citations per response versus 10+ for ChatGPT), focusing on brevity and clarity. It's particularly well-integrated into Microsoft's ecosystem, including Windows, Office applications, and Edge browser.

Google Gemini

Google's Gemini represents their multimodal AI model, capable of understanding and generating text, images, audio, and video. Following the Gemini 2.5 update, it offers strong search and research capabilities, benefiting from Google's expertise in search functionality. Gemini is particularly effective for complex research tasks and integrates well with other Google applications.

Market Share and Adoption

As of 2025, AI search holds 12-15% of global search market share, while traditional search (primarily Google) retains 65-85%, depending on region and use case. However, the trajectory is clear: AI search engine usage is projected to reach over 28% of total global search traffic by 2027. Among demographics, Gen Z and Millennials are the most active adopters, with over 70% using tools like ChatGPT and Perplexity for various tasks.

AI Search Engines vs Traditional Search Engines

Understanding the fundamental differences between AI-powered and traditional search engines helps clarify when to use each approach. The distinction goes far beyond surface-level differences in user interface.

Core Operational Differences

Traditional Search Engines:

Index and rank: Crawl the web, catalog content, and rank pages based on relevance signals like keywords, backlinks, and user engagement metrics
Keyword matching: Primarily match user queries with content containing specified keywords
User does synthesis: Present a list of links; users must click through, read, and synthesize information themselves
Static results: Deliver the same ranked list to all users for a given query (with some personalization)
Goal: Drive clicks to external websites

AI Search Engines:

Understand and synthesize: Use natural language processing to grasp query intent and context, then generate original responses by synthesizing multiple sources
Semantic search: Understand meaning, not just keywords; can handle complex, conversational queries
AI does synthesis: Sift through sources, extract relevant information, and present a coherent answer with citations
Dynamic responses: Generate unique, contextual answers that can vary based on conversation history and user preferences
Goal: Provide direct answers and build authority/trust (clicks are secondary)

Performance and Accuracy

Speed: Traditional search engines like Google typically return results in milliseconds. AI search engines are slightly slower due to the synthesis step—Google AI Mode delivers responses in 0.3-0.6 seconds, while Perplexity averages 1-2 seconds, and ChatGPT may take longer for complex queries.

Accuracy: This is nuanced. Traditional search is highly reliable at finding relevant sources but requires users to evaluate them. AI search engines can make mistakes through "hallucinations"—generating confident-sounding but incorrect information. However, when properly configured with RAG and citations, modern AI search engines achieve impressive accuracy. Studies from 2025 show platforms like Perplexity with error rates around 13% for technical queries, while some Google AI Overview responses showed 26% error rates in specialized domains.

The key factor is source transparency. AI search engines that provide inline citations (like Perplexity) allow users to verify claims, while those that don't can be problematic for critical research.

Use Case Optimization

When to Use Traditional Search:

Local information: Maps, directions, business hours, local services
Shopping: Product comparisons, prices, availability, reviews
Navigation: Finding specific websites, social media profiles, or company pages
Simple factual queries: Quick facts, definitions, conversions where you want multiple sources to choose from
Exploration and serendipity: When you want to discover unexpected connections or browse options

When to Use AI Search:

Research and analysis: Synthesizing information from multiple sources on complex topics
Comparative questions: "What's the difference between X and Y?" or "How does A compare to B?"
How-to and explanations: Step-by-step instructions, concept explanations, troubleshooting
Time-sensitive synthesis: Getting up-to-date summaries of recent events or developments
Multi-step reasoning: Complex queries requiring analysis across multiple domains

Impact on User Behavior

AI search is fundamentally changing how people seek information. According to 2025 studies, 43% of professionals now use ChatGPT for work-related tasks, and 71% of Americans use AI search to research purchases or evaluate brands. More tellingly, 84% of developers now use or plan to use AI tools in their work, with 51% using them daily.

This shift has profound implications. Traditional search drove traffic to websites—the entire SEO industry was built on earning clicks. AI search often provides answers without users clicking through to original sources. Gartner predicts a 50% drop in traditional organic traffic by 2028 due to AI-driven search proliferation.

However, this doesn't mean websites become irrelevant. Instead, the goal shifts from earning clicks to earning citations. Being the authoritative source that AI engines reference builds brand recognition, trust, and influence even when users stay on the search platform.

The Rise of Generative Engine Optimization (GEO)

As AI search engines reshape information discovery, a new discipline has emerged: Generative Engine Optimization (GEO). First introduced in an academic paper by Princeton University researchers in November 2023, GEO represents the evolution of SEO for the AI era.

What is Generative Engine Optimization?

Generative Engine Optimization (GEO) is the practice of optimizing content to appear as authoritative sources or direct responses within generative AI platforms like ChatGPT, Claude, Perplexity, and Google's AI Overviews. While traditional SEO focuses on ranking in search engine results pages to earn clicks, GEO aims to position content as the primary source that AI engines reference when generating answers.

As one industry expert put it: "SEO is about getting found; GEO is about getting featured." This distinction is crucial because generative engines operate on fundamentally different principles than traditional search. They don't prioritize sending traffic to external websites—they synthesize information from multiple sources into single responses, evaluating authority and trustworthiness differently from Google's traditional algorithms.

Why GEO Matters in 2025

The data is compelling. According to recent research:

Generative Engine Optimization delivers up to 4.4x higher conversions than traditional SEO, with a $3.71 ROI per $1 spent
71% of CMOs are reallocating budgets toward GenAI optimization
AI-referred traffic visitors are 4.4x more valuable than traditional search traffic
89% of B2B buyers have adopted generative AI as a key source of self-guided information throughout their purchasing journey
Only 7.2% of domains appear in both Google AI Overviews and LLM results, indicating specialized optimization is needed

Perhaps most significantly, GEO remains an emerging discipline with relatively low competition. Keyword difficulty scores for "generative engine optimization" average just 15-20, compared to 45-60 for equivalent SEO terms. Early adopters are building citation authority that compounds over time—research shows AI models exhibit "source preference bias," favoring sources that have proven reliable for a topic.

Key Differences: GEO vs SEO

Understanding how GEO differs from traditional SEO is essential for developing an effective strategy:

Aspect

Traditional SEO

GEO

Primary Goal

Rank in SERPs to earn clicks

Get cited in AI-generated answers

Success Metric

Rankings, traffic, CTR

Citations, mentions, brand visibility in AI responses

Optimization Focus

Keywords, backlinks, page authority

Semantic clarity, structured data, source authority

Content Approach

Keyword density, comprehensive coverage

Clear structure, answer capsules, conversational language

Value Delivery

Direct traffic to website

Build authority and brand recognition

Core GEO Strategies

Based on research from Princeton University, Georgia Tech, and extensive industry testing, here are the proven strategies for GEO success:

1. Create Answer Capsules

Provide immediate, clear answers to questions at the beginning of your content. For example, if your article addresses "What is Generative Engine Optimization?" start with a concise definition that AI models can easily extract. Research from Backlinko shows pages with answer capsules achieve 40% higher citation rates.

2. Optimize for E-E-A-T Signals

Experience, Expertise, Authoritativeness, and Trustworthiness remain critical. Include transparent author bios, cite reputable sources, and regularly update content. AI engines heavily favor content with clear authority markers.

3. Structure Content for Machine Readability

Use clear headings, bullet points, tables, and schema markup. AI systems parse content more effectively when it follows semantic hierarchies. Include TL;DR summaries, quote blocks, and FAQ sections.

4. Dominate Earned Media

Research shows AI search exhibits systematic bias toward earned media (third-party, authoritative sources) over brand-owned content. Focus on getting cited by authoritative publications, academic papers, and industry leaders. This builds the external validation that AI engines prioritize.

5. Use Natural, Conversational Language

Write as if answering a question directly. AI search queries are typically phrased as complete questions or conversational statements. Content that mirrors this natural language performs better in semantic search.

6. Keep Content Fresh and Updated

AI engines heavily favor recent content, particularly Google's AI Overviews. Regular updates signal that information is current and reliable. Include publication and update dates prominently.

7. Optimize Technical Infrastructure

Ensure AI bots can crawl and access your content. This means fast site speeds, mobile optimization, proper robots.txt configuration, and comprehensive sitemaps. Some platforms offer features like Bing IndexNow API to push priority content directly into indexes.

8. Produce Original Research and Data

Original research, whitepapers, and data-driven content position your brand as an industry authority. AI engines are more likely to cite unique insights and statistics that can't be found elsewhere.

Measuring GEO Success

Unlike traditional SEO where tools like Google Search Console provide clear rankings data, measuring GEO performance requires different approaches:

Citation tracking: Monitor how often your brand appears in AI responses. Tools like Profound, Semrush Enterprise, and specialized GEO platforms track visibility across ChatGPT, Perplexity, and Google AI Overviews.
AI bot traffic: Track referral traffic from ai.com, perplexity.ai, claude.ai, and direct traffic spikes that correlate with AI citations.
Branded search increases: Monitor branded search volume as users who encounter your brand in AI responses often perform follow-up searches.
Source diversity: Track which pages and content types get cited most frequently to inform future content strategy.

The Future of GEO

As we move deeper into 2025 and beyond, GEO will likely become as important as traditional SEO. Industry analysts predict several developments:

Platform-specific optimization: Different AI engines have unique characteristics. GEO strategies will increasingly need to be tailored for ChatGPT vs. Perplexity vs. Google AI vs. Claude.
Multimodal optimization: As AI search becomes more visual and audio-capable, optimization will extend beyond text to images, videos, and voice.
Agentic commerce: AI agents that can browse, compare, and purchase on behalf of users will require new optimization strategies focused on being discoverable and trusted by autonomous agents.
Hybrid strategies: The most successful brands will maintain both strong traditional SEO and GEO, ensuring visibility regardless of how users search.

Mid-market brands typically invest $75,000-$150,000 annually in comprehensive GEO programs, with larger enterprises allocating significantly more. However, even smaller organizations can start implementing GEO best practices immediately at minimal cost by optimizing existing content and focusing on the fundamentals of clear structure, authoritative sourcing, and semantic clarity.

Conclusion: Embracing the AI Search Era

The transformation of search from keyword matching to AI-powered synthesis represents one of the most significant shifts in how humans access information since the birth of the internet. AI search engines and large language models aren't replacing traditional search—they're expanding the landscape, offering new ways to discover, analyze, and verify information.

For businesses, content creators, and SEO professionals, this shift demands adaptation. The skills that built successful SEO strategies remain valuable—authority, quality content, technical excellence, and user focus are still paramount. But these fundamentals must now be applied in new ways, optimizing not just for clicks but for citations, not just for rankings but for AI visibility.

The opportunity is significant. Early adopters of GEO are building citation authority that compounds over time, positioning themselves as trusted sources in their domains while competition remains relatively low. The brands that understand how to create AI-optimized content, build recognized authority across the digital ecosystem, and maintain presence across both traditional and AI search channels will be the ones that thrive.

As search continues evolving, remember that while technology changes rapidly, the fundamentals endure: provide genuine value, demonstrate clear expertise, and meet user needs. Whether those needs are being met through Google's blue links or ChatGPT's synthesized answers matters less than ensuring your content and brand are there to serve them.

The future of search isn't about choosing between traditional SEO and GEO—it's about creating an integrated strategy that ensures visibility and authority wherever and however people seek information. The AI search revolution is here. The question is: will you lead it or follow it?

Key Takeaways

AI search engines use large language models and retrieval-augmented generation to synthesize information from multiple sources, delivering direct answers rather than just links
Major AI search platforms include ChatGPT (700M weekly users), Perplexity (780M monthly queries), Google AI Overviews (55% of searches), and others like Claude and Copilot
AI search currently holds 12-15% of global search market share but is projected to reach 28% by 2027
Traditional search optimizes for clicks; AI search optimizes for citations and brand authority
Generative Engine Optimization (GEO) is the emerging discipline of optimizing content for AI-powered search, delivering 4.4x higher conversions than traditional SEO
Key GEO strategies include creating answer capsules, optimizing for E-E-A-T, structuring content for machine readability, and dominating earned media
Success in 2025 and beyond requires integrating both traditional SEO and GEO for comprehensive search visibility