AI that searches, AI that creates

Large-scale language models know a lot, but they aren't always up-to-date or accurate. Especially in corporate websites, public institutions, and specialized industries, accurate information from clear sources is far more important than "plausible answers." This is where the concept of Retrieval-Augmented Generation (RAG) comes in. RAG is a structure in which AI, rather than relying on memory to generate answers, first retrieves the necessary information (retrieves it) and then generates answers based on that information (generates it). This is why AI search summarization, internal knowledge retrieval, and customer service AI are rapidly shifting to the RAG architecture.

The concept of RAG and recent technology trends

RAG is more of an architectural pattern than a single model. Its basic structure is simple: when a question is asked, AI first searches for relevant documents from external data sources and generates an answer based on the results. Recent trends include:

First, semantic search based on vector databases has become the standard.

Second, enterprise RAGs that integrate websites, CMS, PDFs, and internal documents are spreading.

Third, citation and citationability have emerged as more important evaluation criteria than search accuracy. This is because AI summarization results are beginning to be used in real-world business decision-making.

The Impact of RAG on Corporate Websites

The proliferation of RAG fundamentally changes the role of websites. Websites are no longer mere "bunches of pages for people to read," but rather knowledge repositories that AI searches, understands, and cites. Unstructured HTML, text within images, and information trapped in PDFs are largely unusable in a RAG environment. Conversely, content with well-organized text structure, clear document boundaries, and provenance information becomes a key reference for AI. In other words, the technical design of a website directly determines the quality of AI responses.

Technical issues that are easily missed on websites

Many companies perceive RAG as an “AI issue,” but the actual bottleneck is often on the website.

• Document unit structure is unclear: If multiple topics are mixed on one page, search accuracy drops sharply.
• Meaningless URL and title structure: AI uses URLs, titles, and headings as the identity of a document.
• Providing PDF-centric information: PDF is still important, but from a RAG perspective, it's more of a last resort.
• Absence of source and reference point: Information is less reliable if it is not known when, by whom, and on what basis it was written.
• Content that relies on dynamic rendering: Relying solely on JS rendering increases the likelihood of being missed during the search and crawl phase.

Website Technology Strategies for RAG Response

Responding to RAG at the website level begins with a reorganization of information architecture, not with the introduction of grandiose AI.

First, content must be restructured into RAG-friendly document units (chunks). Ideally, each question corresponds to a single answer.

Second, design a clear heading system (H1–H3) and meaningful URLs.

Third, key information is provided as HTML text, and PDF is used as supplementary material.

Fourth, specify the author, update date, and scope of application to enable AI to determine reliability.

Fifth, design a data structure that assumes search and citation from the CMS stage.

These factors go beyond SEO and are part of the AEO/AI Citation response.

Commonalities seen in reference cases

Organizations that effectively utilize RAG have one thing in common: they are not "companies that excel at AI," but rather "companies that excel at information management." They view their websites, internal documents, FAQs, and policy documents as a single knowledge system. Furthermore, rather than focusing on preventing AI from giving incorrect answers, they focus on structures that prevent it from giving incorrect answers.

Insight Summary

RAG isn't just a simple AI technology trend. It's a shift that redefines the very purpose of websites and their content. Future websites will need to be structured not to simply display more content, but to be more accurately cited. In an era where AI searches for questions and generates answers, websites must be ready to answer.