How AI Search Engines Work

Why Technical Knowledge Matters for GEO

Understanding how AI search engines work technically isn't just academic—it directly informs optimization strategy. When you understand the mechanisms AI uses to find, evaluate, and cite content, you can optimize more effectively than competitors relying on trial and error.

This guide explains the key technologies in accessible terms, focusing on practical implications for marketers rather than deep technical implementation.

Large Language Model Basics

Large Language Models (LLMs) like GPT-4, Claude, and Gemini are AI systems trained on massive text datasets to understand and generate human language. They work by predicting what text should come next given a context.

Training vs. Inference

Training is the process of teaching the model using large datasets. The model learns patterns, facts, and relationships from billions of text examples. Training data has a cutoff date—information after that date isn't in the model's base knowledge.

Inference is when the trained model generates responses to user queries. During inference, the model applies what it learned during training to new inputs.

Retrieval-Augmented Generation (RAG)

RAG solves the knowledge limitation by giving LLMs the ability to "look things up" before answering. Instead of relying solely on training data, RAG-enabled systems search external knowledge sources in real-time.

The RAG Pipeline

Query Processing: The system analyzes the user's question to understand intent and extract key concepts. It may reformulate the query into multiple search queries to capture different aspects.

Document Retrieval: The system searches a knowledge base (often the indexed web) for relevant documents. This uses semantic similarity rather than just keyword matching.

Context Assembly: Retrieved documents are processed and relevant portions are assembled into context that will inform the response.

Response Generation: The LLM generates a response using both its training knowledge and the retrieved context, synthesizing information and potentially citing sources.

Understanding Embeddings

Embeddings are numerical representations of text that capture semantic meaning. They're how AI systems understand that "automobile" and "car" are related concepts even though they're different words.

How Embeddings Work

Text is converted into high-dimensional vectors (lists of numbers). Semantically similar texts have embeddings that are mathematically close together. This enables semantic search—finding documents based on meaning rather than exact keyword matches.

Vector Databases

Embeddings are stored in specialized vector databases optimized for similarity search. When a user query comes in, it's converted to an embedding and compared against stored document embeddings to find the most similar content.

Retrieval Systems

Different AI platforms use different retrieval approaches:

Web Search Integration

Platforms like ChatGPT with browsing and Perplexity search the web in real-time. They issue search queries to search engines (Bing, Google, proprietary indexes) and retrieve results to inform responses.

Proprietary Indexes

Some platforms maintain their own crawled web indexes. This allows more control over what content is available but requires significant infrastructure.

Knowledge Bases

Enterprise deployments often search private knowledge bases—internal documents, databases, or curated content collections.

Hybrid Approaches

Most modern AI search systems combine approaches: using training knowledge for stable information, real-time search for current information, and knowledge graphs for entity understanding.

Response Generation and Citation

Once relevant documents are retrieved, the LLM generates a response. This involves several considerations:

Context Window

LLMs have limited context windows—the amount of text they can consider at once. Retrieved documents must fit within this limit, which means ranking and selection matter significantly.

Source Evaluation

The model evaluates retrieved sources for relevance, authority, and reliability. Not all retrieved documents contribute equally to the response.

Information Synthesis

The LLM synthesizes information from multiple sources, combining facts and perspectives into a coherent response. It may resolve conflicts between sources or present multiple viewpoints.

Citation Decisions

The model decides what to cite and how. Some facts may be stated without citation, others with explicit source attribution. Citation patterns vary by platform and query type.

Platform-Specific Architectures

ChatGPT

Uses GPT-4/4.5 models with optional web browsing via Bing. Training data provides base knowledge; browsing supplements for current information. Plugins and integrations can add additional data sources.

Perplexity

Built search-first with RAG as the core architecture. Uses multiple search sources and always provides explicit citations. Optimized for information retrieval over conversational interaction.

Google AI Overviews

Leverages Google's massive search index and knowledge graph. AI generation overlays on existing Google Search infrastructure, heavily influenced by traditional ranking signals.

Claude

Anthropic's Constitutional AI approach emphasizes safety and accuracy. Can access external information through integrations. Known for nuanced, well-reasoned responses.

Optimization Implications

Understanding these technologies suggests specific optimization strategies: