How AI Search Actually Works: A Technical Deep Dive

When you ask ChatGPT “What’s the best project management tool for small teams?” or query Perplexity about recent developments in quantum computing, something fundamentally different is happening compared to traditional search engines. Behind the conversational interface lies a sophisticated technical architecture that’s redefining how we access information. This deep dive explains exactly how AI-powered search engines work—from the moment you hit enter to when you receive a synthesized, cited answer.

TL;DR: How AI Search Engines Work

• Traditional search ranks pages; AI search retrieves, synthesizes, and cites content from multiple sources to generate one comprehensive answer instead of ten blue links
• RAG (Retrieval-Augmented Generation) is the core architecture that extends large language models with real-time information retrieval, anchoring responses in current, verifiable data rather than relying solely on training data
• Every query goes through five stages: query understanding → information retrieval from vector databases → ranking/re-ranking by relevance → context construction with retrieved passages → answer generation with inline citations
• Content optimization strategy: Optimize for passage-level retrieval (modular, extractable sections), structure for context construction (clear headings, self-contained paragraphs), and write for citation (authoritative sources, statistics, freshness signals)

Key Definitions: Technical Terms Explained

RAG (Retrieval-Augmented Generation): An architecture that extends large language models with real-time information retrieval capabilities. RAG combines a retrieval model (which searches knowledge bases) with a generation model (LLM) to provide answers grounded in specific, current data rather than relying solely on static training data.

Vector Embedding: A numerical representation of text that captures its semantic meaning in high-dimensional space. AI systems convert both queries and documents into vector embeddings to find semantically similar content, enabling “meaning-based” search rather than just keyword matching.

Passage-Level Relevance: AI search engines extract and cite specific paragraphs or sections that directly answer a query, not entire web pages. Most AI citations are driven by passage-level relevance, making modular, self-contained content blocks essential for visibility.

Grounding: The principle of constraining LLM responses to only include information from retrieved sources. Modern AI search follows the rule “you are not supposed to say anything that you didn’t retrieve,” which significantly reduces hallucinations by anchoring answers in verifiable facts.

Re-ranking: A second-pass relevance scoring process applied after initial retrieval. After finding potentially relevant documents through vector similarity, AI systems use more sophisticated scoring to identify the absolute best sources before constructing the final context for answer generation.

What Is the Fundamental Difference Between Traditional and AI Search?

Traditional search engines like Google and Bing operate on a ranking paradigm. They crawl the web, build massive indexes, and when you search, they return a ranked list of web pages based on relevance signals like keywords, backlinks, and user engagement metrics. Your job is to click through and find your answer.

AI search engines operate on a fundamentally different architecture. They don’t just rank—they retrieve, synthesize, and cite. AI search platforms like ChatGPT and Perplexity AI represent a fundamental shift from ‘ranking’ websites to ‘selecting’ content (Pallas Advisory, March 2025). Instead of giving you ten blue links, they give you one answer built from multiple sources.

The RAG Architecture: The Engine Behind AI Search

At the heart of modern AI search engines lies Retrieval-Augmented Generation (RAG)—an architecture that extends large language models with real-time information retrieval capabilities.

What is RAG?

Retrieval-Augmented Generation is a technique that supplements text generation with information from private or proprietary data sources. It combines a retrieval model, which is designed to search large datasets or knowledge bases, with a generation model such as a large language model (Elastic, 2025).

The term was first introduced in a 2020 research paper by Meta (then Facebook), which developed RAG to give LLMs access to information beyond their training data (IBM Research, July 2025). As IBM researcher Guillermo Lastras aptly describes it: “It’s the difference between an open-book and a closed-book exam.”

Why RAG Matters

Large language models are trained on massive datasets, but this training data becomes static the moment training ends. Without RAG, an LLM can’t tell you about events that happened after its training cutoff date, and it can’t access your company’s internal documents or the latest breaking news.

When a large language model doesn’t have enough information or has no contextual knowledge of a topic, it is more likely to hallucinate and provide inaccurate or false responses (Google Cloud Blog, February 2024). RAG solves this by anchoring LLM responses in specific, current, verifiable data.

The Five-Stage RAG Pipeline: How Your Query Becomes an Answer

Every AI search query goes through a sophisticated multi-stage pipeline. Let’s break down each stage with technical detail.

Stage 1: Query Processing and Understanding

When you submit a query to an AI search engine, the first task is understanding what you’re actually asking for.

Query Intent Classification: The system uses small, efficient classifier models to determine the user’s intent and query complexity. As one technical analysis explains, Perplexity uses an intelligent routing system that uses small, efficient classifier models to first determine the user’s intent and the complexity of their query (ByteByteGo, November 2025).

Query Reformulation: For complex queries, the system may break down your question into multiple simpler sub-queries. According to the original GEO research, a query re-formulating generative model generates a set of queries which are then passed to the search engine to retrieve a set of ranked sources (Aggarwal et al., 2024).

For example, if you ask “What are the pros and cons of remote work for tech startups?”, the system might internally generate sub-queries like:

• “Benefits of remote work for tech companies”
• “Challenges of remote work in startups”
• “Remote work productivity studies”

Query Embedding: The query is converted into a vector representation—a numerical form that captures its semantic meaning. The information retrieval model transforms the user’s query into an embedding and then searches the knowledge base for similar embeddings (IBM, October 2025).

Stage 2: Information Retrieval

This is where AI search engines actually “search” the internet or knowledge bases. The retrieval process is remarkably sophisticated.

Vector Databases and Semantic Search: Unlike traditional keyword matching, modern retrieval uses vector embeddings to find semantically similar content. Vector databases store documents as embeddings in a high-dimensional space, allowing for fast and accurate retrieval based on semantic similarity (Google Cloud, 2025).

The critical insight here is that “the question is not the answer.” As Google explains, a question like ‘Why is the sky blue?’ and its answer, ‘The scattering of sunlight causes the blue color,’ have distinctly different meanings (Google Cloud Blog, February 2024). This is why simple similarity search often fails—AI systems need to learn the relationship between questions and answers, not just match similar words.

Hybrid Search Approaches: The most sophisticated systems combine multiple search methods:

• Semantic search using dense vector embeddings (understanding meaning)
• Keyword search using sparse vectors (exact term matching)
• Neural matching to understand “fuzzier representations of concepts”

Advanced search engines like Vertex AI Search use semantic search and keyword search together (called hybrid search), and a re-ranker which scores search results to ensure the top returned results are the most relevant (Google Cloud, 2025).

Real-Time vs. Pre-Indexed Retrieval: Different platforms use different approaches:

• Perplexity performs real-time web searches for every query, ensuring the most current information is available (Medium, June 2025)

• ChatGPT may use a combination of real-time searches and cached/indexed information depending on the query type

• Google AI Overviews leverages Google’s existing massive index with AI-powered synthesis

Stage 3: Retrieval Ranking and Re-ranking

Once relevant documents are retrieved, they must be ranked by relevance. This happens in multiple passes.

Initial Ranking: Retrieved documents are initially scored based on:

• Semantic similarity to the query
• Recency (newer content often preferred)
• Authority signals (domain reputation, author credentials)
• Passage-level relevance (specific sections that answer the query)

As one expert notes, most AI citations in ChatGPT are driven by passage-level relevance—not keyword match (Wellows, October 2025).

Re-ranking: After initial retrieval, RAG systems often employ re-ranking to further refine the relevance of retrieved information (InterSystems, August 2025). This second pass uses more sophisticated scoring to identify the absolute best sources.

Source Limitation: Due to context window constraints and computational costs, most systems limit retrieved sources. As the original GEO research notes, only the top 5 sources are fetched from the Google search engine for every query in typical implementations (Aggarwal et al., 2024).

Stage 4: Context Construction and Augmentation

Now comes the crucial step: assembling retrieved information into a coherent context that will be fed to the LLM.

Prompt Engineering: With the added data from the knowledge base, the RAG system creates a new prompt for the LLM component. This prompt consists of the original user query plus the enhanced context returned by the retrieval model (IBM, October 2025).

This augmented prompt might look something like:

Context: [Retrieved passages from 5 sources about remote work]
Source 1: [Passage about productivity benefits]
Source 2: [Passage about communication challenges]
Source 3: [Recent survey data]
Source 4: [Expert opinion on work-life balance]
Source 5: [Case study from tech startup]

User Query: What are the pros and cons of remote work for tech startups?

Instructions: Generate a comprehensive answer using ONLY the information provided in the context above. Cite sources using inline citations [1], [2], etc.

Strict Grounding Principle: A defining principle of modern AI search is that you are not supposed to say anything that you didn’t retrieve (ByteByteGo, November 2025). This constraint forces the LLM to stay grounded in the retrieved facts rather than relying on its training data, which significantly reduces hallucinations.

Stage 5: Answer Generation and Citation

Finally, the generative LLM creates the answer.

Synthesis: In the generative phase, the LLM draws from the augmented prompt and its internal representation of its training data to synthesize an engaging answer tailored to the user in that instant (IBM Research, July 2025).

The LLM’s task is to:

• Extract relevant information from multiple sources
• Synthesize it into a coherent narrative
• Maintain logical flow while integrating disparate facts
• Add appropriate citations

Citation Mechanisms: This is where AI search distinguishes itself from traditional chatbots. To enforce this principle and provide transparency, a crucial feature is the attachment of inline citations to the generated text. These citations link back to the source documents, allowing users to verify every piece of information (ByteByteGo, November 2025).

Citation patterns vary significantly by platform:

• Perplexity: Provides inline citations throughout its responses, allowing users to verify information easily (Pallas Advisory, March 2025). Citations are numbered and clickable, linking directly to source pages.

• ChatGPT: It typically cites the most significant sources contributing to the answer, although this might not always be explicitly listed (Pallas Advisory, March 2025). Citations may appear at the end or inline depending on the interface.

• Google AI Overviews: Shows sources at the top or side of the generated answer with visual previews.

Platform-Specific Architectures: How Different AI Search Engines Differ

While all AI search engines use variations of RAG, each has distinctive technical characteristics.

Perplexity: The Citation-First Architecture

Perplexity is fundamentally an ‘answer engine’ that conducts live web searches for every query, providing responses with direct citations to authoritative sources (AllAboutAI, November 2025).

Key Technical Features:

1. Model-Agnostic Routing: Perplexity uses a heterogeneous mix of models, including in-house fine-tuned models from the ‘Sonar’ family and third-party frontier models from leading labs like OpenAI (GPT series) and Anthropic (Claude series) (ByteByteGo, November 2025). The system intelligently routes queries to the appropriate model based on complexity.

2. RAG-Heavy Design: Its RAG process is highly visible, with an explicit focus on showing sources and allowing users to guide the retrieval process (via ‘Focus’ and Pro model selection) (Medium, May 2025).

3. Conversation Context: The system maintains conversation history, allowing follow-up questions without repeating context.

ChatGPT: The Versatile Generalist

ChatGPT combines web search capabilities with its massive pre-trained knowledge.

Key Technical Features:

1. Hybrid Knowledge Base: ChatGPT can draw from both its training data and real-time web searches, depending on the query.

2. Query Intent Understanding: The system first determines whether web search is needed. Queries about recent events, current data, or specific facts trigger search; creative or analytical tasks may rely solely on training data.

3. Publisher Partnerships: ChatGPT has established specific partnerships with publishers for content access, influencing what sources are available.

Google AI Overviews: The Index Advantage

Google’s AI-powered search leverages the company’s decades of search infrastructure.

Key Technical Features:

1. Massive Pre-Indexed Content: Unlike platforms that search in real-time, Google already has billions of pages indexed, dramatically speeding up retrieval.

2. RankBrain and Neural Matching: Google’s proprietary AI ranking systems, developed since 2015, provide sophisticated relevance scoring (Google Cloud Blog, February 2024).

3. Integration with Knowledge Graph: Google can supplement web results with structured data from its Knowledge Graph for entities, facts, and relationships.

What Gets Cited: Platform-Specific Source Preferences

Recent research analyzing 680 million citations across AI platforms reveals striking patterns in what sources each platform prefers.

Citation Patterns by Platform

ChatGPT:

• Wikipedia is the most cited source at 7.8% of total citations, demonstrating the platform’s preference for encyclopedic, factual content over social discourse (Profound, August 2025)
• .com domains represent over 80% of citations
• Authoritative, long-form content dominates

Perplexity:

• Reddit emerges as the leading source at 6.6% of citations
• There’s a unique concentration in community platforms with high credibility given to user-generated discussions (Profound, August 2025)
• Academic and research sources heavily weighted

Google AI Overviews:

• Reddit also prominent at 2.2% of top citations
• More balanced distribution across platforms compared to other AI engines (Profound, August 2025)
• Strong preference for established media outlets

Domain Authority Patterns

With .com domains representing over 80% of citations and .org sites being the second most cited, authoritative domain presence remains crucial. However, newer TLDs like .ai show growing traction, suggesting emerging opportunities for tech-focused brands (Profound, August 2025).

The Citation Selection Algorithm: What Makes Content “Citable”

Understanding why certain content gets cited while other content doesn’t requires examining the multi-factor ranking signals AI systems use.

Primary Ranking Signals

1. Passage-Level Relevance: Most AI citations in ChatGPT are driven by passage-level relevance—not keyword match (Wellows, October 2025). The system looks for specific paragraphs or sections that directly answer the query.

2. Recency: For time-sensitive topics, newer content is strongly preferred. AI sees the date and chooses fresher content every time when recent information is available (Search Engine Journal, September 2025).

3. Authority Signals:

   • Clear authorship with credentials
   • Citations to other authoritative sources
   • Domain reputation
   • HTTPS and verified domain structures

4. Structural Clarity:

   • Clear headings and organization
   • Modular, extractable passages
   • Schema markup for machine readability

5. Factual Density: Content that provides new, verifiable information—what Google has described as ‘information gain’ performs better (Go Fish Digital, September 2025).

What Gets Excluded

Systems actively filter out:

• Content behind paywalls (unless partnerships exist)
• Pages blocked by robots.txt
• Sites with poor trust signals
• Heavily commercialized content without substance
• Content that lacks clear sourcing

The Technology Stack: What Powers AI Search

Behind these systems is a sophisticated technology stack:

Core Components

1. Foundation Models: GPT-4, Claude, Gemini, or proprietary models trained on massive text corpora

2. Vector Databases:

   • Stores embeddings of web content
   • Enables fast semantic similarity search
   • Examples: Pinecone, Weaviate, Milvus

3. Search Infrastructure:

   • Web crawlers
   • Document indexers
   • Real-time fetch systems

4. Orchestration Frameworks: LLM orchestration frameworks such as the open source LangChain and LlamaIndex or IBM watsonx Orchestrate govern the overall functioning of an AI system (IBM, October 2025).

5. Embedding Models: Convert text into vector representations

   • Dense vectors for semantic meaning
   • Sparse vectors for keyword identity

The Computational Challenge

Perplexity’s core technical competency is not the development of a single, superior LLM but rather the orchestration of combining various LLMs with a high-performance search system to deliver fast, accurate, and cost-efficient answers. This is a complex challenge that needs to balance the high computational cost of LLMs with the low-latency demands of a real-time search product (ByteByteGo, November 2025).

What Are the Limitations of AI Search Engines?

Despite their sophistication, AI search engines face several technical limitations:

1. Hallucinations Persist

RAG does not prevent hallucinations in LLMs. According to Ars Technica, ‘It is not a direct solution because the LLM can still hallucinate around the source material in its response’ (Wikipedia, November 2025).

Even with retrieved facts, LLMs can:

• Misinterpret context
• Make connections that don’t exist in the source material
• Be overconfident in synthesizing contradictory information

2. Context Window Constraints

LLMs have finite context windows (typically 4,000-200,000 tokens depending on the model). This limits:

• How many sources can be retrieved
• How much detail can be provided
• The ability to synthesize very large documents

3. Source Access Limitations

Without specific training, models may generate answers even when they should indicate uncertainty (Wikipedia, November 2025). Additionally:

• Paywalled content is often inaccessible
• Real-time data from some platforms may be blocked
• Robot.txt restrictions limit crawling

4. Retrieval Quality Dependence

As RAG-based approaches have grown in popularity, it’s become clear that a RAG system’s efficacy is completely dependent on the search quality of the backend retrieval system (Google Cloud Blog, February 2024). If retrieval returns irrelevant results, even the best LLM will produce poor answers.

5. Freshness-Accuracy Tradeoff

Real-time search provides current information but may surface lower-quality sources. Pre-indexed content is more reliable but potentially outdated.

The Future: Agentic Retrieval and Advanced RAG

The field is rapidly evolving with new architectures emerging:

Agentic Retrieval

Microsoft’s Azure AI Search now provides agentic retrieval, a specialized pipeline designed specifically for RAG patterns. This approach uses large language models to intelligently break down complex user queries into focused subqueries, executes them in parallel, and returns structured responses optimized for chat completion models (Microsoft, 2025).

Unlike traditional RAG where the system executes one retrieval step, agentic systems can:

• Plan multi-step research strategies
• Execute parallel queries
• Synthesize across multiple retrieval rounds
• Self-correct based on initial results

Multi-Modal RAG

Future systems will retrieve and synthesize across:

• Text documents
• Images and videos
• Audio content
• Structured databases
• Real-time data streams

Multi-modal embeddings can be used for images, audio and video, and more and these media embeddings can be retrieved alongside text embeddings or multi-language embeddings (Google Cloud, 2025).

Improved Grounding Metrics

Google’s Vertex Eval Service now scores LLM-generated text on metrics like coherence, fluency, groundedness, safety, instruction_following, and question_answering_quality. These metrics help you measure the grounded text you get from the LLM (Google Cloud, 2025).

Practical Implications: What This Means for Content Creators

Understanding this architecture reveals why certain optimization strategies work:

Why Citations Matter

Since AI systems explicitly check for authoritative sources to include in context, AI can’t trust what it can’t verify. Source your facts and sign your name (Wellows, October 2025).

Why Structure Matters

Because retrieval works at the passage level, content needs to be modular. Keep passages modular and extractable (lists, tables, FAQ-style blocks) so they can be reused directly by LLMs (Go Fish Digital, September 2025).

Why Statistics Work

Fact-dense content with specific data points gives the LLM more concrete information to cite. Google’s patents and AI findings consistently highlight the importance of fact-rich, authoritative content (Go Fish Digital, September 2025).

Why Freshness Matters

Because retrieval systems often weight recent content heavily, regular updates are critical for maintained visibility.

Conclusion: The Architecture of the Answer Engine

AI search represents a fundamental rethinking of information retrieval. By combining the semantic understanding of large language models with the real-time factual grounding of web search, systems like ChatGPT, Perplexity, and Google AI Overviews create something genuinely new—answer engines that synthesize knowledge rather than merely pointing to it.

The five-stage pipeline—query understanding, retrieval, ranking, context construction, and generation—works together to transform questions into cited, synthesized answers. Each stage involves sophisticated AI techniques, from vector embeddings to neural ranking to constrained generation.

As IBM Research succinctly puts it: RAG allows LLMs to go one step further by greatly reducing the need to feed and retrain the model on fresh examples. Simply upload the latest documents or policies, and the model retrieves the information in open-book mode to answer the question (IBM Research, July 2025).

For anyone building content for this new paradigm, the message is clear: optimize for the retrieval stage, structure for the context construction stage, and write for the citation stage. The future of search isn’t about ranking first—it’s about being worth citing.