How AI Search Engines Work: From Google SGE to Perplexity — The Architecture Behind Generative Search

AI-powered search engines like Perplexity, Google Search Generative Experience (SGE), and SearchGPT don't just return a list of blue links. They generate comprehensive, cited answers by combining web retrieval with large language model synthesis.

1. The Generative Search Pipeline

[ User Query: "Best database for RAG applications" ]
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[ Query Decomposition ] --> Sub-queries: "vector databases comparison", "RAG performance benchmarks"
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[ Web Retrieval ] --> Fetch top 20 results per sub-query
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[ Cross-Encoder Re-Ranking ] --> Score relevance of each passage
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[ LLM Synthesis ] --> Generate answer with inline [1][2][3] citations
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[ Citation Verification ] --> Validate each claim maps to a source

2. Building Your Own Generative Search

async function generativeSearch(query: string) {
  // Step 1: Retrieve
  const webResults = await searchAPI.search(query, { count: 15 });
  const passages = webResults.map(r => ({ text: r.snippet, url: r.url }));

  // Step 2: Re-rank
  const ranked = await crossEncoder.rerank(query, passages, { topK: 5 });

  // Step 3: Synthesize
  const answer = await llm.chat({
    system: "Synthesize a comprehensive answer. Cite sources as [1], [2], etc.",
    user: `Query: ${query}\n\nSources:\n${ranked.map((p, i) => `[${i+1}] ${p.text}`).join("\n")}`
  });

  return { answer, sources: ranked.map(p => p.url) };
}

Understanding generative search architecture is essential for anyone building AI-first information retrieval products.