How search engines use AI: crawling, indexing, retrieval and ranking
Search engines use automated pipelines for crawling, indexing, retrieval and ranking. AI techniques such as neural language models and dense passage retrievers improve how queries match documents. Public documentation from Google, Stanford, IBM and Microsoft describes these systems and the datasets used to evaluate them.
Crawling and indexing in modern search
Google's in-depth guide describes how search engines begin by discovering URLs through automated programs called crawlers, such as Googlebot, which follow links and render JavaScript to see page content Google's in-depth guide.
After a page is fetched, the engine analyzes its text, images and videos, then stores signals in a large database called the index. The same guide explains that Google also detects duplicate or similar pages and selects a canonical version to show in results.
Not every page is crawled or indexed; site access rules, robots.txt and content quality affect whether a page enters the index.
Information retrieval: from sparse to dense representations
Classic retrieval systems index terms and apply term weighting methods, as covered in the Stanford Introduction to Information Retrieval book's chapters on scoring and term weighting Stanford IR book. IBM's overview adds that indexing, weighting and relevance feedback are common techniques across retrieval models IBM's information retrieval overview.
Traditional models such as TF-IDF and BM25 rely on exact term matches and sparse vector representations. The paper on dense passage retrieval shows that learned dense embeddings from a dual-encoder framework can outperform a strong Lucene-BM25 system by 9 to 19 percent in top-20 passage retrieval accuracy on open-domain question answering arXiv paper on dense passage retrieval.
This shift toward neural retrieval uses embeddings that capture semantic similarity, allowing queries to match passages even when they do not share exact words.
Ranking with AI systems
Google's ranking systems guide describes automated systems that look at many factors and signals about hundreds of billions of pages and other content to rank results in a fraction of a second. It names BERT as an AI system that understands how combinations of words express meaning, and MUM as a model for specific applications Google's ranking systems guide.
The Google How Search Works site explains that Search organizes information, ranks results, tests changes and detects spam, while keeping a neutral approach that does not accept payment for rankings Google's How Search Works.
These ranking systems incorporate link analysis, freshness and deduplication to present the most relevant results.
Datasets for training and evaluating search AI
Microsoft's MS MARCO project provides large-scale datasets for passage and document ranking, derived from real Bing queries and human-generated answers. It has been used in the TREC Deep Learning tracks and includes millions of passages and documents Microsoft's MS MARCO datasets.
Such datasets enable training and benchmarking of neural retrieval models. The dense passage retriever introduced in the arXiv paper was evaluated on multiple open-domain question answering benchmarks.
Public datasets and standardized evaluations help measure progress in retrieval quality and provide common ground for comparing approaches.
| Model | Representation | Strength |
|---|---|---|
| TF-IDF | Sparse term-frequency vectors | Simple lexical matching |
| BM25 | Sparse with term saturation and length normalization | De facto baseline for retrieval |
| Dense passage retriever | Learned embeddings from dual-encoder | Handles semantic similarity |
Frequently asked questions
How does Google crawl websites?
Google uses automated crawlers such as Googlebot that follow links and render JavaScript to discover and fetch pages. The crawl process is described in Google's in-depth guide to how Search works.
What is the difference between retrieval and ranking?
Retrieval selects candidate documents that match a query, while ranking orders those documents by predicted relevance. Classic retrieval uses term matching, whereas ranking systems like Google's BERT handle meaning and intent.
How do dense passage retrievers improve search?
Dense passage retrievers learn embeddings that capture semantic similarity, so they can match queries to passages without exact word overlap. Research on the arXiv shows that this approach can outperform BM25 in retrieval accuracy.
What role do datasets like MS MARCO play?
MS MARCO provides large real-world query and passage collections for training and evaluating retrieval models. It has been used in TREC Deep Learning tracks and supports research on deep learning in search.
Why does Google use BERT in ranking?
BERT helps Google understand how combinations of words express different meanings and intent, improving the ability to match queries with relevant pages. This is explained in Google's ranking systems guide.