When you ask ChatGPT why it recommended your competitor instead of you, the response you get isn’t some secret peek behind the curtain. It’s just a summary of generic marketing advice that already exists online. ChatGPT doesn’t have any special insight into how it works.
At least once a week, someone sends me a conversation where they asked ChatGPT to explain why it recommended a competitor. They treat it like a revelation. But the process ChatGPT used to explain that recommendation is the same process it uses for every response: it draws from patterns in its training data, applies reinforcement learning, and sometimes searches the internet for more information.
None of that gives it a working understanding of its own architecture.
How ChatGPT Actually Generates Recommendations
ChatGPT is a large language model, not a search engine with a transparent ranking algorithm. It generates answers token-by-token based on learned probabilities. When asked about its “algorithm,” it can only draw on what it learned during training or from user instructions.
Research on large language models shows they can appear to demonstrate introspection, sometimes making assertions about their own thought processes. But this is misleading. Models are trained on text that includes examples of introspection, so they learn to sound introspective even though they don’t truly understand their own states.
When ChatGPT explains why it made a recommendation, it’s fabricating a rationale by summarizing information from its knowledge and the conversation context. It’s not retrieving an internal log or performing true self-analysis.
What Actually Influences ChatGPT Recommendations
If your competitor shows up in ChatGPT and you don’t, it’s not because ChatGPT is playing favorites. It’s because your competitor had stronger signals in the model’s data.
According to research from Onely, ChatGPT typically names only 2-4 brands in a recommendation. This creates a winner-take-all dynamic where only the most visible brands fill those limited slots.
The signals that matter are different from traditional SEO:
Authoritative mentions carry significant weight. One analysis found roughly 41% of recommendations came from placements on authoritative lists, 18% from awards, and 16% from reviews.
Wikipedia and major publications are heavily referenced. ChatGPT leans on Wikipedia and major news outlets for information. If your competitor has a well-maintained Wikipedia page or press coverage and you don’t, the model may see them as more relevant.
Traditional SEO signals have less impact. ChatGPT doesn’t rely on backlinks, domain authority, or keyword rankings the way Google does. About 28% of pages ChatGPT cited in one study had zero Google visibility, meaning brands can dominate AI answers without dominating Google search.
Why ChatGPT’s Self-Explanations Are Unreliable
When you ask ChatGPT how to improve your visibility in its recommendations, it will repeat generic SEO and marketing strategies it learned from the internet. Build your online presence. Get more reviews. Improve your content.
These aren’t diagnostics. They’re just commonly repeated advice.
Some guides suggest ChatGPT cross-references Google Business Profile data, online reviews, and evidence of niche expertise. But these recommendations are largely speculative. ChatGPT doesn’t actually rely on traditional SEO signals.
If ChatGPT suggests publishing an “llm.txt” file or similar hacks, that’s a red flag. Those ideas come from rumor, not from actual model design.
The Role of RLHF in Recommendations
Reinforcement Learning from Human Feedback (RLHF) is a core part of how ChatGPT was trained to be helpful and aligned. But it’s not a brand-ranking mechanism.
As OpenAI describes it, RLHF makes the model safer, more helpful, and more aligned by using human feedback on its responses. It shapes how ChatGPT answers questions, not what content it has learned from the web.
RLHF doesn’t insert a hidden scoring function for advertisers. It doesn’t come with a built-in list of preferred brands. Any insight ChatGPT seems to give about recommendations is derived from data patterns it learned, not from RLHF itself.
Does ChatGPT Search the Internet to Explain Recommendations?
If you’re using ChatGPT with browsing enabled, it can search the web when answering questions. When asked to justify a recommendation, it may look up relevant information and cite sources.
But this still isn’t introspection. The model isn’t examining its prior answer. It’s gathering more data to respond to your follow-up. If it cites web sources in its explanation, it’s because it searched after the fact, not because it checked an internal record.
What Actually Works for AI Visibility
Rather than asking ChatGPT to reveal its secrets, focus on the factors that research shows actually matter:
Get featured on authoritative lists. Industry roundups and “best of” lists carry significant weight in AI recommendations.
Pursue mentions, not just links. In AI results, mentions of your company name may be even more important than backlinks. It’s also much easier to get mentioned than to get linked to.
Track AI sources. Instead of just checking which companies ChatGPT recommends, identify the websites it references when making those recommendations. Those sources become your target list for visibility efforts.
Maintain up-to-date content. Recent, high-quality content on authoritative platforms helps establish your presence in the model’s knowledge.
Stop Asking ChatGPT for Answers It Can’t Give
The next time you’re tempted to ask ChatGPT why it recommended your competitor, remember: the response will just echo standard marketing advice it learned from training data. It won’t be a revelation. It won’t be a secret formula.
If you want to improve your visibility in AI results, focus on the signals that actually matter: authoritative mentions, presence in the publications and directories these models reference, and genuine reviews from real customers.
ChatGPT doesn’t know how ChatGPT works. Neither do most SEOs. But we do know what’s working right now, and it starts with understanding where these models look before making recommendations.TJ Robertson is the founder of TJ Digital, an AI optimization agency helping small to medium businesses get recommended by ChatGPT and other AI platforms. Get in touch to learn how we can help your business show up in AI results.