From SEO and AEO to GEO: A necessary shift in the modern journalistic editorial process.
The rise of large language models (LLMs) and AI-integrated search engines is changing how readers access information. This article analyzes the shift from traditional search engine optimization (SEO) to responsive engine optimization (AEO) and generative engine optimization (GEO), and proposes necessary changes in editorial processes at newsrooms today to ensure AI systems prioritize information retrieval and citation.
Tạp chí Khoa học và Công nghệ Việt Nam•16/10/2026
Shifting keyword search trends to answer-based searches.
For over two decades, SEO (Search Engine Optimization) has been a crucial component of digital journalism publishing. Newsrooms are concerned with titles, keywords, descriptions, URLs (Uniform Resource Locators) – the identifying address of a resource on the Internet, internal links, and the ability to crawl and index to help articles appear in a favorable position on search results pages. However, journalism trends have changed with the development of AI in recent years [1].
SEO is still necessary because content that is not discovered and indexed by search engines will be difficult to reach readers from the search environment. However, the emergence of Google AI Overviews, ChatGPT, Gemini, Perplexity and similar tools is adding a new intermediary layer between thed present information instead of just returning a list of links [2-4]
Meanwhile, GEO (Generative Engine Optimization) goes further by placing content into a search environment and synthesizing it using AI generation. With a complex question, the system can combine multiple sources, multiple pieces of information, and multiple entities to form an answer. For the editorial office, the question is no longer just whether the article ranks highly on the search results page, but also which information in the article is clear enough, has sources, is valuable, and has context to be recognized, retrieved, or referenced by the system when creating an answer [3, 4].
AEO (Answer Engine Optimization), a new SEO trend, provides direct, concise, and easy-to-understand information right on the search results page or through virtual assistant devices.
Although all aim to improve content accessibility in the digital environment, SEO, AEO, and GEO differ in their objectives, optimization focus, and how users receive information. Clearly distinguishing these three approaches helps newsrooms identify the necessary additions during the content organization and publishing process. The fundamental differences are summarized in Table 1.
A significant shift in the editing process.
The core shift from SEO to AEO/GEO doesn’t lie in adding a few techniques after the article is finished. The more significant change occurs within the editorial process itself. While the SEO-oriented approach often begins with the question, “What keywords are users searching for?”, the new process requires additional questions: What do readers actually want answered? What information in the article can be independently accessed? What sources support that information? And what value does the article offer that differs from existing content on the internet?
In this approach, the article is not just a complete block of text for the reader, but also a collection of informational units with clear structure, source, and context. A paragraph explaining a concept, a statistic, an expert statement, or specific information is clear enough that when extracted from the entire article, it is still possible to identify what it is about, who provided it, and when it was presented. This is the shift from optimizing a single page of content to organizing and managing journalistic knowledge.
Another requirement is to increase the value of original information. As AI becomes capable of rapidly synthesizing knowledge already available on the Internet, an article that merely reproduces existing content will find it increasingly difficult to stand out. The value of professional journalism lies in original information: expert interviews, data from authoritative bodies, technical documents, surveys, experiments, data collected by the editorial office, or the author’s own analysis. Along with that, each important piece of data needs to answer three basic questions: whose data it is, when it was published, and what scope it reflects [3].
This shift is not only reflected in the presentation of an article but impacts every stage of the editorial process, from topic selection, structure building, content production and validation to publication and performance measurement. Compared to the SEO-centric approach, AEO/GEO places additional demands on information needs, data sources, entities, context, and content accessibility. This shift is specifically illustrated in Table 2.
Incorporate AEO/GEO into the manuscript creation, editing, and publishing process.
Given the actual impact of AI in modern journalism [5], establishing the basic newsroom process to effectively utilize AEO and GEO includes the following steps:
Step 1 – Identify information needs.
Based on the proposed topic, the editorial team identifies the questions readers need answered and the core information the article must clarify. Compared to an SEO-centric approach, this requirement expands the focus from “which keywords are searched” to “which questions need to be answered.”
Step 2 – Organize the structure and identify the sources for each piece of content.
The content is divided into clearly related sections; the necessary data, documents, and expert sources are identified accordingly. This organizational approach helps the article remain coherent for the reader while preventing information from becoming detached from its
Step 3 – Create content with unique value.
In addition to background information, articles need to be reinforced with original information, reliable data, expert opinions, and independent analysis. This is what makes a difference as AI systems become increasingly capable of synthesizing popular content.
Step 4 – Edit and proofread.
This is the editorial process in the direct sense; editors need to check the accuracy, logic, language, terminology, data, proper names, sources, and consistency of the information; and ensure that important information can be traced back to its source.
Step 5 – Check AEO/GEO before publishing.
This should be a separate review process, rather than a general requirement that articles must be “AI-friendly.” Editors can use a standardized checklist to review the title, introductory paragraph, headings, links, author information, metadata, and other necessary data to help search engines and AI systems correctly identify the article’s topic
Step 6 – Monitor and update.
After publication, the editorial team monitors reach and updates on current events. Post-publication data serves as a basis for adjusting content organization, but should not be considered the sole measure of journalistic quality.
Set up a checklist before publishing.
To make AEO/GEO a viable part of the editorial process, a pre-publication checklist is a crucial tool for achieving clearer, more transparent, and more verifiable results in articles. The following suggested checklist can be directly integrated into the editorial form or the editorial office’s publishing management system:
Title : Clearly states what problem the article addresses.
Sapo : The most important information from the article has been summarized.
Heading : If you read each heading separately, can you identify the topic being discussed?
Paragraphs in the article : Is important information placed in easily accessible locations, or is it lost in lengthy explanations?
Terminology : Were the key technical concepts clearly explained upon their first appearance?
Entities : Are the names of people, agencies, organizations, products, technologies, documents, and abbreviations accurate and consistent?
Data : Is it possible to identify whose data it is, when it was published, and within what scope?
Links : Has the article linked to they?
Time : What are the publication date, update date, and time of the data’s relevance?
Metadata and structured data : Are the title, description, author, content type, publication date, and update date fully declared?
The results of AI-based visibility assessments should be considered as a basis for identifying limitations and improving the quality of content and publishing platforms. Therefore, the transition from SEO to AEO and GEO is not simply about changing techniques and technologies for optimization, but more importantly, about transforming editorial thinking, from optimizing article visibility to organizing journalistic content with a clear structure, transparent sources, and verifiable accuracy.
In an environment where AI is increasingly involved in information retrieval and consumption, the core values of journalism remain accuracy, reliability, originality, and analytical quality. Therefore, the ultimate goal is not to “write for AI,” but to improve the quality of content organization, editing, and publication, making information both easily accessible, recognizable, and citationable on digital platforms, while ensuring its long-term usability and relevance to readers.
[1] Nguyen Nhu Tuan (2026), “10 main trends of scientific journals in 2026”, Information Security Journal , 2(90) , pp.47-50, https://antoanthongtin.vn/tin/10-xu-huong-chinh-cua-tap-chi-khoa-hoc-trong-nam-2026, accessed on 09/10/2026.
[2] EM Bender, T. Gebru, AM Major, et al. (2021), On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?, Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT), pp.610-623, DOI: 10.1145/3442188.3445922.
[3] Google Search Central (2023), “Google Search’s guidance about AI-generated content”, https://developers.google.com/search/blog/2023/02/google-search-and-ai-content, accessed 9 October, 2026.
[4] P. Aggarwal, V. Murahari, T. Rajpurohit, et al. (2024), GEO: Generative Engine Optimization , Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’24), pp.5-16, DOI: 10.1145/3637528.3671900, accessed 9 October, 2026.
[5] Nguyen Nhu Tuan (2026), “Specialized journals in the era of artificial intelligence“, Information Security Journal , 4(92) , pp.35-37, https://antoanthongtin.vn/tin/tap-chi-chuyen-nganh-trong-ky-nguyen-tri-tue-nhan-tao, accessed on 09/10/2026./.
Original article
https://vjst.vn/tu-seo-aeo-den-geo-buoc-chuyen-dich-can-thiet-trong-quy-trinh-bien-tap-bao-chi-hien-dai-103594.html
Tag:Editing processGEOdigital pressscarOptimizing generation toolsAEOPublishing contentOptimize response tools
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