Search engines are the gateways we use to search, find, and view textual, visual, video, code, and other sorts of data on the internet. Thanks to search engines, we can access a wide range of information from the nearest pet shop to DIY (do it yourself) guides. Developing AI technology aims to provide users a better experience by integrating into search engines, like every digital product. If you are wondering what a search engine is and how it can be combined with AI, we’ve got you covered!

In this article, we will explore search engines and discuss how AI supports them.

Ready? Let’s dive in!

TL; DR

  • Search engines are tools that allow users to find the information they are looking for online.
  • Search engines work by analyzing indexes that match users' keywords.
  • Search engines use the PageRank system, which includes keywords, algorithms, and hyperlinks, to rank results.
  • AI search engines are tools that use the capabilities of artificial intelligence to power search engines.
  • The best AI search engines include Google, Bing, and Perplexity.
  • TextCortex offers its users a web search feature that allows them to chat with any webpage or access summaries of websites that match their keywords.
  • ZenoChat by TextCortex offers its users knowledge bases powered by internal or specific data searching.

What is a Search Engine?

A search engine is software that helps users find any type of information online. Search engines use the text entered in their type-box as keywords and sort through online content to present it to the user. Search engine result pages (SERPs) vary according to the user's location, keyword, search history, and parameters. In addition, the most popular content containing the keywords entered by the user is presented to the user. This content can vary from web pages to blogs, images to videos. Search engines use web crawlers to find descriptions that match users' search terms.

How Do Search Engines Work?

Search engines use three basic stages to search for what users type into the text box:

  1. Crawling to discover content
  2. Indexing to track and store content
  3. Bringing relevant content

The first stage of the search engine working principle, crawling to discover content, is where search engines read the metadata and descriptions of online content to discover what the content is about. In the second stage, the index where the information of online content is stored is matched with the keywords the user typed into the search box. In the last stage, the content with the most relevant index is presented to the user.

How Do Search Engines Rank Results?

Search engines use different ranking methods to bring webpages, videos, blogs, and other sorts of data sources to users. These methods both prioritize the user's search and aim to provide relevant and useful content to the user. The ranking of results by search engines includes the following parameters:

  • Algorithms: Search engines use formulas called algorithms to understand whether a website is noteworthy. This ranking stage is classified according to the index analysis results of website crawlers.
  • Keywords: Keywords include words, phrases, terms, and letters that users enter into search boxes. If a website has the keyword that the user is searching for, it is marked as relevant content.
  • PageRank: Search engines analyze the hyperlinks in the content to measure the relevance of online content to keywords. The more relevant and strong hyperlinks an online content has, the more it is ahead in search results.

The Impact of AI on Search Engines

AI models, which are used in different ways in most digital products and strengthen their various aspects, are also used to enhance the capabilities of search engines. For example, Google aims to offer more personalized and relevant SERPs to users and improve users' search engine experience by integrating its own Gemini AI model into its search engine.

Artificial Intelligence can increase the efficiency and accuracy of search engines by analyzing textual data with technologies such as machine learning, deep learning, and natural language processing. According to Statista's research, approximately 402.74 million terabytes of data are created and added to online resources daily. AI's data analysis capabilities are used to analyze such large data and find resources that contain the information users are looking for. Thus, search engines can utilize AI to both understand users' intent and analyze large amounts of data to provide relevant content to users.

AI Search Engine Examples

The term AI Search Engine represents search engine applications that enhance the capabilities of traditional search engines with artificial intelligence features and aim to provide a better experience to their users. AI search engines can be used for a wide range of purposes, from daily tasks to professional searches. Let’s take a look at the examples of AI search engines.

Google

Google, the world's most popular and frequently used search engine, offers users a generative AI-powered search engine experience by integrating AI's unique capabilities into its search engine. By integrating generative AI into its search engine, Google can understand users' complex search terms, categorize large amounts of online content, and provide results that include the information the user needs. In addition, Google uses AI to enhance users' shopping experience by creating personalized shopping graphs and preparing search results that include the descriptions, images, user reviews, ratings, and prices of the products you search for.

Bing AI

Bing AI, which became popular as the first search engine and app to integrate OpenAI’s GPT-4 model and is familiar to every Windows user, is a good example of how search engines can use AI. Bing AI search engine provides conversational searches using artificial intelligence and offers an experience where you can get information through chatting instead of examining SERPs. Bing AI is an all-in-one AI search engine that offers not only an AI search engine but also text, code, and image generation. While Google uses AI only to improve search engine result pages, Bing offers its users improved SERPs, content generation, and an AI chatbot.

Bing AI Search Engine

Perplexity

Perplexity is an AI search engine that allows users to perform online searches in a conversational format. The most distinctive feature that distinguishes Perplexity from other AI search engines is its focus feature. With this feature, you can choose the content type that Perplexity will focus on during the search crawling phase. Perplexity’s focus feature, which allows you to search specific data, includes the following source options:

  • Web
  • Academic
  • Math
  • Video
  • Social

In addition to these options, Perplexity offers an AI text generation feature with a “Writing” option. If you want to generate content such as blog posts, essays, and emails by prompt entry, you can use this option.

Perplexity AI Search Engine

TextCortex – Enhance Your Search Engine Experience

If you are looking for an AI assistant that can both enhance your web search experience and generate output using specific data, TextCortex is designed for you, with its advanced features such as web search, and chatting with URL/domain/webpage. TextCortex offers its users features such as multiple LLMs, web search, knowledge bases, knowledge management, AI templates, and an AI writing assistant. TextCortex is available as a web application and browser extension. The TextCortex browser extension is integrated with 30,000+ websites and apps to support you ANYWHERE and ANYTIME.

ZenoChat – Web Search

TextCortex offers a multifunctional conversational AI called ZenoChat. ZenoChat is a conversational AI assistant that offers text/code/image generation, paraphrasing, translation, and web search features that allow you to use cutting-edge LLMs such as GPT-4 and Claude 3.5 Sonnet. With ZenoChat’s web search feature, you can access up-to-date relevant information without having to deal with search engines and SERPs. Moreover, ZenoChat allows you to verify the accuracy of the information by citing sources such as websites that it uses to generate web search outputs. ZenoChat’s web search allows you to search specific data sources, such as:

  • Internet
  • Twitter
  • Reddit
  • Wikipedia
  • YouTube
  • Scholar
  • News

In addition to these options, ZenoChat offers “This page” and “This domain” options that allow you to chat with specific web pages or websites. With these options, you can learn what a website is about without having to examine it completely and without dealing with ads. This feature is especially useful for quickly analyzing web pages that contain long content.

Knowledge Bases - Search From Internal Data

Another data search enhancement that ZenoChat offers its users is knowledge bases. With ZenoChat knowledge bases, you can integrate your specific data sources into ZenoChat, connect them, and have ZenoChat generate output using only this data. If you want to search among your internal data, ZenoChat’s Knowledge Bases are your savior.

what is an AI search engine?

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