The technology space is pretty obsessed with preventing code defects from getting to production. We take great pains to make sure that a mistake doesn’t make it from the developer’s fingertips all the way through to the product system.

There’s an entire field called DevOps (short for “development operations”). This is something like a $5 billion industry. There are entire market segments filled with companies that tightly control the movement and testing of code.

Search for “DevOps diagram” sometime. You’ll be amazed at what you find—detailed schematics showing exactly how code should be copied, packaged, tested, and deployed. Developers who don’t have an artistic bone in their bodies suddenly turn into Da Vinci when describing in exacting detail how they want to orchestrate code deployments.

All of this is in search of one goal: prevent bad code from reaching production. A lofty goal, to be sure.

…but why don’t we care so much about content?

Where we have majestic acrobatics on the code side, when it comes to content, the process is usually something like, “Well, Alice writes something in Word, then emails it to Bob, and he copies it into the rich text editor” then presses publish.

Congratulations, you have the tightest, most reliable codebase serving up terrible content. A+. Great job.

Content defects are a thing, and we don’t do enough to prevent them. In particular, we don’t look at content development as a process to be managed. We think it’s some kind of magic, not a flow of work with checkpoints, trackable assignments, and review gateways. We’re somehow convinced this would take the “soul” out of it or something.

So, while our developers get six figures worth of toys to make sure they can swap every line of code instantly without spilling their coffee, our content creators are copying and pasting things into Slack and yelling “I swear sent that to you last week!” over the cubicle wall.

We need to do better.

Content creation isn’t magic—no more than code is magic. It’s a process that can and should be managed just like code deployments, and it deserves the same level of regard.

Your content creators need:

• Library services. Your developers have source code management. They know where code is, all the time. They probably have versions of it dating back to when they were teenagers. These things exist for content as well—they’re called content marketing platforms (CMPs) and digital asset management systems (DAMs). They’re designed to store, organize, and version content assets so creators know where everything is.
• Change management, in the form of editorial calendaring. Your developers know when code will be released (note: don’t do it on Fridays). They plan these things long in advance. But ask a content creator when Content Item X for the new campaign is launching, and they can only say something like, “I don’t know. I showed it to Bob. It’s in his court now…”
• Workflow. Developers have detailed ticket management systems that can tie their actions down to the exact line of source code they changed to resolve a defect. These systems exist so that everyone knows, at all times, who is responsible for what. Meanwhile, the content editors can only shrug when someone asks who was supposed to edit the CEO’s blog post that she just announced from the keynote stage.
• Content preview. I promise you that your development team has a graduated system of environments where they test code. They probably spend hundreds of hours maintaining it, so they can run code in isolation and know exactly how it works before they deploy it. Think of that fondly next time when your image caption is published in 30pt bold-faced font because no one told you that it wouldn’t be. (Incidentally, I’ve been thinking about preview a lot lately.)

Here’s why this is important:

Content defects matter. They can be far more damaging than code defects, while being so much harder to detect. By the time you realize something is wrong, the problem may have been existing in public for a long time, doing a lot of damage.

Imagine that you have a software company, and you’ve been trying to get an analyst to include your software in one of their reports. Your Analyst Relations staff has been consistently courting, cajoling, and hinting to this analyst that your software fits their segment exactly, and would be a great addition to the report.

The analyst finally decides to check things out. They go to your website, looking for evidence of all the things you told them about. They expected to find reinforcement of that information, that energy, that…vibe.

But, they didn’t. Their experience fell flat. They gave you a 20-minute chance, but then clicked away and didn’t look back.

Oh sure, you had plans. You were going to revamp that part of the website, and you had mentioned it to Gary just before he went on vacation. You heard some rumors that people were working on it, and some content got changed, but you never saw and never had a chance to guide it. Content development seemingly happened in a far-off land somewhere. Normally, when something changed on the website, you were as surprised as anyone.

This is a content defect. The whole thing. One big defect.

Why don’t we categorize like this? Why don’t we call it what it is?

Maybe because it’s not…binary? With code, things often either work, or explode spectacularly, so we can stand back and confidently say, “Yup, that’s busted.”

But with content, there’s a spectrum—there’s a range. People can look at it and say, “yeah, that’s fine” even when it’s not.

The only solution here is process. You need a way to make sure that content is seen by the right people, and at the right time, and has a way of reflecting the right input.

This happens with code all the time. We handle code exactingly, rigorously, and with due process and care.

We need to demand the same for content. And we need to start acknowledging that poor content is a failure of process, a failure of planning, and a failure of tooling.

The tools are available to avoid this. We need to implement them and use them.

Interested in learning how Optimizely Content Marketing Platform can better support your content creation process? See how it works in this quick video.

There are several AI chatbot-like features available in the current search engines, including Bing Copilot, Google, Bard, and Gemini. They help to optimize the content visibility in the search results with the help of an AI-powered Search engine known as a Generative engine or AI Search.

A traditional search engine like Bing, Google, or Yahoo ranks and displays information in the SERPs based on the search terms a user inputs. 🔎

The generative engine, on the other hand, generates comprehensive, accurate, and relevant results and information with the help of Generative AI or Large Language Models (LLMs) such as chatGPT, Gemini, and Claude. They understand and integrate information from various sources for the user’s queries.

In this blog, We will discuss the GEO that is introduced in the new research, its framework, and how it can change traditional Search engine optimization (SEO) practices and optimize content for visibility.

The Key Components of the GEO Framework and How They Transform Traditional SEO Practices

GEO is described in the research paper as: “A novel paradigm to aid content creators in improving the visibility of their content in Generative Engine responses through a black-box optimization framework for optimizing and defining visibility metrics. We facilitate systematic evaluation in this new paradigm by introducing GEO-bench, a benchmark of diverse user queries across multiple domains, coupled with sources required to answer these queries.”

Traditional SEO depends upon the keyword volume, difficulty, and optimization for the specific search terms, which focus less on an interpretation relationship between the concepts of keywords or user queries. SEO practices prioritize text-based source content over other sources of content format where regular updating of fresh content is not a primary focus. Also, metrics like impression and click rates affect ranking system results in traditional methods.

GEO encourages detailed information over just the keyword, addressing the related main queries by creating depth content and potential subtopics with the understanding of concept and relationship, encouraging the other formats, such as visual, audio, and images, not just text-based. Moreover, it emphasizes the latest updated content information with continuous accuracy and relevance to provide the most accurate and up-to-date details.

The Impact of Introducing GEO on Website Ranking and Content Relevancy

A generative engine relies on traditional SEO practices like user intent and algorithms for ranking to a degree, such as keyword stuffing. Although it focuses on keywords, it tries to find connections and meanings beyond the keywords in order to create high-quality content.

GEO doesn’t directly indicate the web visibility or page ranking in the Search Engine Result Page. However, it can optimize the overall website visibility and indirectly drive user traffic to your websites through generated responsive data and information.  

GEO-optimized content provides the AI Search or a Large Language Model (LLM) with reliable and completely detailed information, enabling them to generate the most accurate and relevant information for responses to user questions or inputs.

These AI-powered engines can deliver a vibrant user experience using optimized content for user engagement and interactive experiences. Furthermore, It also builds trust with a user as it relies on renowned and credible sources, which enhances the effectiveness and reliability of the generated response data and provides synthesizing information.

Comparison with Existing SEO Models: Why GEO Stands Out in Enhancing Search Engine Performance

GEO utilizes auto-generative algorithms for content generation based on predetermined objectives and standards where generated content can cover a broader range of keywords and related topics in various formats like image and visual.

A generative search engine uses modern optimization techniques that involve cognitive SEO, NLP (natural language processing), and structured data markup to maintain and improve content leverage, relevancy, and search engine visibility. In addition, it introduces new methods for determining citations’ importance and website visibility, as well as improving user-centric content by using impression metrics.

Traditional SEO models rely upon and use specific keywords to optimize and rank manually in search results. It uses traditional optimization techniques like link building, meta tags, and URLs.

In traditional search optimization, content creation and optimization can be slow and have low content scalability compared to AI-powered, requiring manual effort for generation and optimization. Constant monitoring and adaptation to platform algorithms are needed to produce the latest and updated information for dynamic user behavior.

Both are equally responsible for improving the brand or website’s online visibility; traditional SEO models require the manual touch for content creation and optimization. GEO tends to use generative responses automatically for content generation as per user queries, making it more effective for user-centric content creation, optimization, and stability in related topics or keywords.

9 Test research findings to improve the website content in GEO

The researchers from Princeton University, Georgia Tech, Allen Institute for AI, and IIT Delhi tested nine various GEO approaches to improve site content for generative engines. Techniques that have been tried and tested over 10,000 search queries, nine optimization strategies were tested on something that “closely resembles the design of BingChat”:

1. Authoritative: The content was altered to be more compelling while conveying definitive claims.
2. Keyword Stuffing: More keywords were added to match the query.
3. Statistics Addition: Instead of a qualitative conversation, quantitative statistics were included.
4. Sources: Relevant citations have been added. Like quotes statistics
5. Quotation Addition: Quotations from reliable sources have been included.
6. Easy-to-understand: Simplified the language.
7. Fluency Optimisation: Improved fluency.
8. Unique Words: Used in the text whenever possible.
9. Technical terms: Technical terms have been incorporated into the content.

The data set for search queries was obtained from Google, Microsoft Bing, and Perplexity. Sources include AI Discover, GPT-4, and others.

So, focus on creating detailed and comprehensive blogs or articles by defining the relation and highlighting the context for deeper meaning. Utilize the various formats for content creation to enrich information and diversify the learning perspective.

Also, update your content with the latest information and trends to maintain regular effectiveness and relevancy in the generative engines.

Conclusion:

In the end, Generative Engine Optimization (GEO) provides a more automated, scalable, and adaptive method of content creation and optimization than traditional Search Engine Optimization (SEO) approaches, which need manual and constant work for the optimization and ranking. Compared to traditional search engines, generative engines give instant and detailed personalized information to users’ queries for improved engagement.

Conventional SEO uses metrics like impression, session duration, and click-through rate (CTR), whereas GEO proposes new metrics to measure the relevance and visibility of citations within generative engine responses, making users eliminate the need to visit individual websites for information as it generates the responses on users queries from the reliable, relevant, and various sources.

AI-powered search optimization is still developing and becoming popular since most users and business owners are using generative AI as their source of information and improved visibility with universally applicable diverse content formats.