Unveiling LLM-Enhanced Search Technologies

Author(s): Florian June

Originally published on Towards AI.

Principles, Key Features and Insights

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In the internet age, the explosion of information has created a growing need for efficient content retrieval. Search engines like Google, Bing, and DuckDuckGo build on text-based keyword search to deeply analyze webpage links and assess their relevance.

Figure 1: Comparison between traditional search engine and LLM-enhanced search. Screenshot by author.

In the past two years, the integration of LLMs, RAG, and Agent technologies has brought search engines into a new era. The next generation of search engines emphasizes user experience, better understands semantic context, and offers advanced features such as multi-turn conversational Q&A, personalized recommendations, and multimodal and cross-language retrieval. This shift allows search engines to provide direct, concise conversational answers rather than merely presenting a list of webpage links.

This search approach that combines LLM, RAG, and Agent technologies lacks a standardized name. Some refer to it as AI-powered Search, while others call it Conversational Search or LLM-powered Search. From a technical perspective, this article terms it LLM-enhanced search.

As shown in Figure 2, the traditional search engine workflow consists of three main steps:

Collecting and processing vast amounts of internet data;Creating indexes and developing retrieval algorithms for quick information discovery;Processing user… Read the full blog for free on Medium.

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Published via Towards AI