What Is AI Search? How It Works, Finds Sources, and Generates Answers

AI search finds information by having an AI system interpret your question, retrieve relevant sources, and write a direct answer, usually with citations. Underneath, AI search is a retrieval pipeline wrapped around a language model, so answer quality depends on which pages it retrieves and reads.

The language model is usually a large language model (LLM), a program trained on huge amounts of text to understand and write language. Everyday examples of AI search engines include Google AI Overviews and AI Mode, ChatGPT search, and Perplexity. Some people call these tools answer engines, because they put an answer first and links second.

Usage numbers for AI search are reported by the companies themselves. One headline number is Google's 2.5 billion AI Overviews users, from a June 2026 update: "AI Overviews now have over 2.5 billion users each month."

Another is OpenAI's 900 million weekly users: in February 2026, OpenAI wrote, "ChatGPT is where people start with AI, with more than 900M weekly active users." That figure covers all ChatGPT use, so it measures more than search alone.

How AI Search Differs From Traditional Search

Traditional search matches your keywords against an index and returns a ranked list of links, while AI search reads several sources and writes one answer. An index is a large catalog of web pages that a search engine has already collected and organized. Because an LLM reads the whole question, AI search can understand intent in conversational search, where you type a full question.

The table compares both approaches on six dimensions. The last row explains a lot about accuracy, because each approach tends to fail in a different place.

The main consequence for websites is fewer clicks from search results. Per Pew Research on AI summaries, "Users who encountered an AI summary clicked on a traditional search result link in 8% of all visits. Those who did not encounter an AI summary clicked on a search result nearly twice as often (15% of visits)."

Pew's numbers come from US adults' Google visits in March 2025. The study is observational, so it shows a link between AI summaries and fewer clicks without proving that the summaries cause the drop.

How AI Search Works, Step by Step

AI search works as a pipeline of six steps, and each step can succeed or fail on its own. A strong model still struggles to recover when an earlier step feeds it the wrong page.

Step 1: Understanding and Expanding the Question

The system first rewrites your question into clear search queries, and it often splits one question into several smaller searches. This splitting is called query fan-out: one question fans out into many sub-queries that can run at the same time.

For example, "best laptop for video editing under $1,500" might fan out into separate searches for recommended specs, current prices, and recent reviews. Natural language processing, the part of AI that handles human language, helps the system work out what each part of the question means. A vague or wrong sub-query at this step sends every later step in the wrong direction.

Step 2: Retrieving Candidate Sources

Each sub-query runs against a web index or a search API, which returns a ranked list of URLs with titles and short descriptions. A search API is a service that lets software send a query and get results back as data.

Semantic search ranks results by meaning, so a search for "cheap flights" can match a page about "low-cost airfare." Vector search makes that possible by turning text into lists of numbers, called vectors, and finding pages whose numbers sit close together.

Retrieval decides which pages the model ever sees. Developers can run this step with a web search API for agents. Olostep's Search API (POST /v1/searches) returns deduplicated links, each with a url, title, and description.

Step 3: Fetching and Cleaning Page Content

The system then fetches each candidate page and pulls out its main text. JavaScript-heavy pages may need browser rendering, which means loading the page in a real browser so its scripts can build the content first.

Raw HTML also contains ads, menus, cookie banners, and scripts that take up space the model could use for real content. Cleaning converts that HTML into plain text, Markdown, or structured JSON that keeps the content and drops the clutter. A short guide on what LLM-ready data means explains why that format matters for language models.

Pages that block access, time out, or fail to parse drop out of the answer, even when they hold the best information. A scrape API handles this step. Olostep's Scrape endpoint can turn pages into LLM-ready markdown, and it can also return HTML, plain text, or JSON.

Step 4: Ranking the Most Useful Passages

The system ranks passages by first splitting each cleaned page into chunks, which are short sections of text a few paragraphs long. A reranker, a second model that scores how well each chunk answers a sub-question, then moves the best chunks to the top.

Only the top chunks move forward, because the model has a limited context window. The context window is the maximum amount of text an LLM can read at one time. A relevant passage ranked just below the cutoff never reaches the model.

Step 5: Generating a Grounded Answer

The LLM writes the answer using the top-ranked passages as its source material. This approach is called retrieval-augmented generation (RAG): retrieve relevant text first, then generate an answer from it.

The original RAG paper (Lewis et al., 2020) reported that "RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline." In plain terms, a model that looked things up wrote more factual text than a model relying only on what it memorized in training.

Grounding means tying each statement in the answer to a specific piece of retrieved text. RAG and grounding can reduce hallucination, a false statement the model presents as true, but they do not remove it. The model can still misread a passage or fill a gap with a guess.

Step 6: Citing the Sources

The system attaches source URLs to the statements they support. This works only if each chunk stays linked to its original URL through fetching, cleaning, chunking, and ranking. If that link breaks at any step, the answer may cite the wrong page or a page that says something different.

This bookkeeping between passages and URLs is called citation mapping. The six steps fit together in this order:

1. Understand and expand the question into sub-queries.
2. Retrieve candidate URLs from an index or search API.
3. Fetch and clean each page into text, Markdown, or JSON.
4. Rank chunks and keep the ones that fit the context window.
5. Generate an answer grounded in those chunks.
6. Cite the source URL behind each statement.

Where AI Search Gets Its Information

AI search gets its information from three places: the model's training data, a pre-built search index, and live retrieval at question time. A single product can combine all three, and the mix decides how fresh an answer can be.

Training data is the text a model learned from before release. It is fixed at a knowledge cutoff, the date after which the model has seen no new information.

A search index is a stored copy of web pages that crawlers refresh on a schedule. A crawler is a program that visits pages and follows their links to collect more pages. Teams building their own AI search often start by crawling websites for RAG, which collects many pages from a single start URL.

Live retrieval fetches pages at the moment you ask. It is the freshest option, but it adds time and cost to every question.

Time-sensitive questions need live retrieval. Facts like today's exchange rate or this week's product launches can change within hours. An answer built only on training data or an old index may miss those changes.

Why AI Search Answers Can Be Wrong

AI search answers can be wrong because errors can enter at any step of the pipeline. Columbia's Tow Center for Digital Journalism tested eight AI search tools on a news-citation task in early 2025. According to the Tow Center AI citation study, "Collectively, they provided incorrect answers to more than 60 percent of queries."

Each tool received excerpts from news articles and had to identify the original article, publisher, and URL. The result describes that specific task, so it does not measure accuracy on every kind of question.

Some errors are hallucinations. Per OpenAI research on hallucinations, "Hallucinations are plausible but false statements generated by language models."

Many other errors start before the model writes a word. Each failure below maps to one step of the pipeline:

• Wrong sub-query: The system misreads the question during fan-out and searches for the wrong thing.
• Wrong or stale page: Retrieval returns an off-topic page or an outdated version of the right one.
• Unfetchable page: A blocked or JavaScript-heavy page fails to load, so its information never reaches the model.
• Syndicated copy: The answer cites a republished copy of an article in place of the original source.
• Misattributed citation: A passage loses its link to its source, so the citation points to the wrong URL.
• Model gap-filling: The model guesses when the retrieved passages don't fully answer the question.

Clean page text and preserved source URLs can reduce citation errors, as explained in this guide to web scraping for RAG. For anything important, open the cited source and confirm it says what the answer claims.

How Developers Add AI Search to Their Own Products

Developers add AI search to apps and AI agents by calling the same pipeline through APIs. Each type of API covers a different part of the work:

• Search API: Discovers relevant URLs for a query and returns links with titles and descriptions.
• Scrape API: Reads one page and returns it as Markdown, HTML, plain text, or JSON.
• Crawl API: Collects many pages from a start URL to build an index or a RAG dataset.
• Answers API: Runs search, reading, and synthesis in one call and returns the answer with its sources.

Olostep's Answers endpoint provides grounded answers with citations in a single request. Olostep's Answers FAQ explains that the agent searches the web, reads relevant pages, and synthesizes an answer, optionally as structured JSON.

The request needs a task, which is your question in plain language. An optional json field supplies a schema that asks for the answer in a specific JSON shape, which is easier for code to use.

import os
import requests

response = requests.post(
    "https://api.olostep.com/v1/answers",
    headers={"Authorization": f"Bearer {os.environ['OLOSTEP_API_KEY']}"},
    json={
        "task": "In what year was Python first released, and who created it?",
        "json": {"first_release_year": "", "creator": ""},
    },
    timeout=60,
)

print(response.json())

Illustrative sample response (simplified to show the structure; check the Answers documentation for exact field names):

{
  "result": {
    "json_content": {
      "first_release_year": "1991",
      "creator": "Guido van Rossum"
    },
    "sources": [
      "https://docs.python.org/3/faq/general.html",
      "https://en.wikipedia.org/wiki/Python_(programming_language)"
    ]
  }
}

The sources array inside result lists the URLs used to generate the answer, so your app can show citations or let users check them. Olostep notes that a request can take up to about 30 seconds, because the agent searches and reads live pages before answering.

That wait fits research tasks and background agents better than features that need an instant reply. The Answers endpoint works with publicly accessible web data, and Olostep's terms require users to respect each site's access rules.

What AI Search Means for Websites and Brands

For websites and brands, being cited in AI search depends first on being retrievable. Pages that are fetchable, clearly structured, and specific are easier for retrieval systems to fetch, chunk, and match to a sub-query.

Brands can also test whether AI engines mention or cite them for relevant questions. Olostep's AI visibility checker is one way to check your AI visibility for your brand.

Frequently Asked Questions About AI Search

Is AI search the same as a chatbot?

No: a chatbot can answer from its training data alone, while AI search retrieves sources for each question and usually cites them. Some chatbots, including ChatGPT, add a search mode that combines both.

What is the difference between AI search and semantic search?

Semantic search is a retrieval method that finds pages by meaning, while AI search is the full system that retrieves sources and writes an answer. Many AI search systems use semantic search inside their retrieval step.

Can you trust AI search results?

AI search results are a useful starting point, but you should verify anything important by opening the cited source. Answers tend to be more reliable when they cite specific, current pages that contain the exact claim.

Can I turn off AI search in Google?

According to Google Search Help, AI Overviews are a core Search feature and "cannot be turned off." Google says you can select the Web filter after a search, which "displays only text-based links without features like AI Overviews."

What is agentic search?

Agentic search is AI search run by an AI agent that plans several rounds of searching, reads results, and decides what to look up next. It suits multi-step research questions that one round of retrieval can't answer well.

How fresh is the information in AI search?

Freshness depends on the source: training data stops at a knowledge cutoff, while live retrieval reads pages when you ask. For time-sensitive questions, check the dates on the cited pages.