How Website Search Works (Without the Jargon)
Search can sound technical, but the basic pieces are easy to grasp. Understanding them helps teams make better trade-offs when choosing or tuning a search solution.
Crawling and extraction
The first step is collecting content: HTML pages, PDFs, Markdown files, PDFs, and embedded media. Crawlers or connectors pull that content and extract the useful text and metadata (titles, headings, tags). Good extraction keeps structure (headings, lists, code blocks) so search can return precise snippets.
Embeddings: representing meaning
Embeddings convert text into numeric vectors that capture meaning. Similar sentences produce similar vectors even when the wording differs. This is the foundation of semantic search: retrieve content that's conceptually relevant, not just term-matching.
Vector search and ANN
Vector search finds nearest vectors to a query vector. For large collections, approximate nearest neighbor (ANN) algorithms provide fast results with acceptable accuracy trade-offs. The engine returns candidate passages ranked by similarity scores.
Hybrid ranking and business signals
After candidates are retrieved, a ranking step orders results using a combination of semantic scores and business signals: exact-term matches, freshness, manual boosts, click data, and user permissions. This hybrid approach balances relevance and reliability.
Answer cards and result presentation
Instead of a flat list of links, modern search surfaces answer cards, highlighted snippets, and direct actions (download, contact support, open settings). Good presentation reduces friction and speeds task completion.
Feedback loops and tuning
Search improves when teams instrument it: capture queries, CTRs, zero-result rates, and downstream task completion. Use that data to add synonyms, retire stale content, adjust boosts, and measure improvements via A/B tests.
Breaking search into crawling, representation, retrieval, ranking, and UI makes it easier to prioritize improvements. Focus on the weakest layer first — often indexing or presentation — and iterate with data.
Understand search mechanics before choosing tools or tuning relevance.
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