At the Core of GEO and Going Global Is Making Good Products Accurately Readable
GEO is not a speculative new traffic play, but foundational infrastructure that uses structured content and layered protocols so AI and search engines can accurately read real products.
I read @HiTw93's article "The GEO You Don't Know: Principles, Practices, and Trade-offs of AI Visibility" along with a curated resource list on building websites for overseas markets. What I agree with most is the author's pragmatic judgment on how AI retrieval works; the specific script recommendations are secondary.
Lately, whenever people talk about taking indie products global and acquiring traffic, Generative Engine Optimization (GEO) and AI search come up frequently. Some treat GEO as a new kind of traffic secret, trying to figure out how to use specific prompt tricks to "fool" large models. To me, the priority for investing time here is clear: GEO is worth spending an hour to get your structured standards right, but not worth spending a week trying to game the system.
Retrieval logic in the AI era has indeed changed, yet I think the underlying common sense remains the same: the key to going global is still whether a product hits a real pain point, while GEO is about establishing a machine-readable standard so truly valuable products and content can be clearly and accurately read and cited by AI search systems.
From "Pleasing Ranking Algorithms" to "Designing for Semantic Understanding"
Traditional SEO revolved around competing for the top spots on search engine results pages (especially the top 10 on Google). Fighting for those positions spawned a host of mechanical optimization tactics across the industry, such as keyword stuffing and link farming.
Following the underlying logic of AI retrieval, I have noticed that the rules have shifted in several ways:
- Non-linear distribution of citations: Relevant industry observations point to a representative data point: among the sources cited by systems like Google AI Overview, up to 83% come from web pages outside the original top 10 rankings. When synthesizing answers, large models do not solely look at a domain's PageRank authority; they also look for clearly structured, fact-specific passages that directly answer a user's concrete question.
- Opportunities for small, long-tail projects: As I understand it, as long as a small-scale project, or even a solo-maintained open-source tool or indie product, has well-standardized documentation and clear positioning, AI will actively identify it and recommend it to users as a typical solution in many niche scenarios where big websites offer vague, hollow content.
- High conversion and small volume: Visitors coming from AI retrieval usually arrive with clear intent, and the conversion rate of this referral traffic is noticeably higher than that of traditional broad search; however, across total web referral traffic right now, the absolute share brought by AI search remains very low (under 1%).
So treating GEO as a short-term windfall of traffic is dangerous, whereas treating it as essential baseline visibility infrastructure for your site offers a very high return on effort.
Three Key Engineering Practices for Implementing GEO
Combining industry best practices with my own notes, improving an overseas-facing site's visibility in the AI era comes down to doing three standardized tasks well:
1. Precisely Separate Crawler Types in robots.txt
Many sites tend to go to one of two extremes when handling AI scraping: either blocking everything outright or leaving the doors wide open without protection. My suggestion is to apply fine-grained tiering based on your business goals:
- Search and real-time retrieval crawlers (such as
OAI-SearchBot,Claude-SearchBot,PerplexityBot): These fetch the latest information when a user initiates a live search. In principle, you should allow them access; otherwise, your site will be directly invisible in AI search; - User-initiated conversation crawlers (such as
ChatGPT-User,Claude-User): These are used when an end user pastes a link to your website into a chat window and asks the AI to parse or summarize it. You should let them through to ensure a normal sharing experience for users; - Foundation model training crawlers (such as
GPTBot,CCBot): These specifically scrape web-wide data to train underlying models. Whether you allow them depends on your commercial strategy regarding content copyright and training licensing; - Opt-out declarations and unspecified crawlers (such as
Google-Extended, etc.): These can be used to proactively declare that your content will not participate in model training.
By separating "allowing retrieval and citation" from "allowing use for model training," I think you can benefit from the exposure AI search brings while guarding against unchecked data scraping.
2. Deploy llms.txt as the Primary Entry Point for Machines
Much like robots.txt is meant for search engines, llms.txt is a navigation standard prepared specifically for large language models. Placing a concise, clear Markdown specification file in your website's root directory significantly reduces the overhead a model faces when trying to understand your site's overall architecture:
- State clearly in one sentence what the project actually is and what core problem it solves;
- Map out links to the most essential documentation, API specifications, and Pricing pages;
- Flag reference pages suitable for authoritative citation.
These steps are straightforward and proactively lower the cognitive load on an Agent parsing your web pages, so there is no need to treat them as mysterious tricks.
3. Dedicated Pages per Project and Full Semantic Markup
AI systems rely heavily on contextual self-consistency. Cramming all your information onto a single homepage makes it hard for models to extract distinct attributes:
- Create a dedicated landing page for each core product: Clearly explain the target audience, typical use cases, core feature comparisons, and a clear FAQ;
- Semantic formatting: Use a clear heading hierarchy and favor structured lists over long, bloated blocks of narrative;
- Complete metadata: Fully configure Schema.org structured data, OpenGraph, and Twitter Card tags so that both human social sharing and machine scraping receive consistent semantic facts.
Going Global Also Requires a Complete Commercial Conversion Loop
What GEO standards solve is making sure that "when someone asks an AI a question, your product isn't inexplicably left out." For an overseas project to survive, I think you still have to run through the full commercial conversion loop:
- Validating real demand: Are overseas users genuinely willing to pay to solve this pain point? Is the intent behind the search queries casual browsing, or a strong willingness to pay?
- Fast launch and feedback: Can you ship an MVP as quickly as possible into the hands of real users, and do the page load speeds and interaction experience meet the bar?
- Smooth payment experience: Have you integrated mainstream overseas payment channels, and does your pricing model (subscription, one-time purchase, or usage-based credits) match how frequently users actually use the product?
- Data-driven reviews: Periodically reverse-check the quality of Q&A about your product across mainstream AI tools, observe whether they accurately restate your product positioning or cite outdated, incorrect information, and use those findings to feed back into iterating your on-site content.
How I Would Allocate My Time
When building for global markets, placing blind faith in so-called algorithmic hacks often distorts your execution.
I would first build a product that solves a real problem, and then write up the product information clearly enough for machines to read. Spend an hour or two organizing crawler rules, writing a clean llms.txt, and formatting key pages into machine-readable structures, then put your time back into refining the product, serving real customers, and validating cash flow.