I Found an Opportunity to Get Cited in OpenAI Yesterday. Today, It Was Gone

Written by Masab Gadit

Masab Gadit

Founder and CEO

I'm Masab Gadit, Founder and CEO of Wellows, where we're building the future of AI-driven marketing automation. Over the past decade, I've dedicated myself to building and scaling digital businesses—from running one of the most efficient affiliate content networks to now leading an AI-first marketing platform that's reimagining how brands achieve visibility in the age of LLMs. My journey began with mastering Google SEO, affiliate growth, and systemized content operations. But as the digital landscape evolved, I recognized a fundamental shift: we're moving toward a world where AI-first engines like ChatGPT, Gemini, Claude, and Perplexity are reshaping how discovery happens. This realization led to Wellows—a platform where we've envisioned to create Autonomous Marketers that don't just execute tasks, but strategize, create, optimize, and continuously self-improve. These AI agents learn from user feedback, adapt to your brand's unique data, and become star performers on your growth team. At Wellows, we're not just helping brands grow; we're building the Visibility Stack that ensures brands stay cited, discoverable, and trusted across both traditional search engines and emerging AI platforms. Our vision is simple: every brand, regardless of size, should have access to its own self-learning, always-on growth engine. We're building toward a future where marketing automation isn't just about efficiency—it's about creating meaningful connections and sustainable growth.

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5 min read November 6, 2025

The Hard Truth: Yesterday I saw us in OpenAI. Today we were gone. That’s not a metaphor. It’s literally what happened when I searched the same query on consecutive days. One day, Wellows showed up as a citation in ChatGPT’s response. 24 hours later? Different answer. Different sources. We’d vanished.

So now I’m sitting here with a question that probably keeps a lot of you up at night: Should we chase every AI citation opportunity or just ignore them when they disappear?

Let me back up and tell you why this matters and what the research says. Alongside external studies, we also validated these patterns with our own dataset. In our large-scale ChatGPT citation study, we analyzed 7,785 queries and 485,000+ citations to understand which domains win LLM mentions.

The Reality: LLM Answers Are Volatile By Design

Key Insight: LLM outputs are probabilistic, not deterministic. That means they’re supposed to change.

When you type the same query into ChatGPT, Gemini, or Perplexity on different days, you’re not guaranteed the same answer. In fact, you shouldn’t expect it. Here’s why:

1. Retrieval-Augmented Generation (RAG) Systems Rotate Sources

Most modern LLMs don’t just rely on their training data they pull real-time information from the web using RAG. This process involves:

Breaking your query into multiple synthetic sub-queries (a technique called “query fan-out”)
Retrieving passages from dozens of web pages based on semantic embeddings, not just keywords
Reranking those passages probabilistically before synthesizing an answer

iPullRank's deep dive on AI search architectureType the same question into Google’s AI Overview today and tomorrow, and you may not see the same citations.

Research Finding: Running identical queries on different days produced completely different sets of sources not minor variations, but fundamentally different citations.

2. Models Get Updated Silently and Frequently

OpenAI, Anthropic, and Google don’t announce every tweak. According to OpenAI’s Model Release Notes, models receive continuous updates to behavior, retrieval logic, and ranking algorithms.

Sometimes these are major version bumps (GPT-4 → GPT-4o); other times, they’re silent backend changes that shift how sources are weighted and that’s exactly why an AI citation opportunity can appear one day and vanish the next.

Scientific Evidence: A study published in Nature Scientific Reports on LLM consistency found that even with identical prompts, output variability ranged from 9-11% across sessions and that’s before considering retrieval changes.

3. Temperature and Sampling Add Randomness

Even when the same sources are retrieved, LLMs use sampling parameters like temperature and top-p to generate responses. Higher temperatures increase diversity; lower temperatures favor predictability. But even at temperature=0 (the most deterministic setting), there’s still residual randomness in token selection.

IBM's explanation of LLM temperatureTemperature controls the randomness or creativity generated by LLMs during inference.

Translation: Even “factual” answers can vary in phrasing, emphasis, and citation choice.

4. Context and User State Matter

Google’s patent on Stateful Chat Systems (US20240289407A1) reveals that search results now incorporate:

Your prior queries in the session
Device type and location
Engagement history
Real-time server load

Reality Check: Two people asking the same question? Different answers. Same person, different device? Different answers. Even the time of day can shift which model variant handles your query.

But Here’s Where It Gets Interesting

Despite all this volatility, some patterns are predictable. What looks like chaos at first glance is actually patterned behavior driven by how LLMs interpret, retrieve, and reward structured content.

Princeton University StudyA study on Generative Engine Optimization (GEO) found that certain content strategies increased citation visibility by up to 40% across multiple LLM runs. That tells me volatility isn’t random noise, it’s structured randomness.

Think of it like this: LLM citations aren’t like SEO rankings (deterministic, relatively stable). They’re more like slot machine probabilities, you can’t control individual spins, but you can engineer the odds in your favor

The iPullRank Framework: From Rankings to Probabilities

Mike King’s team at iPullRank broke down the shift from traditional SEO to what they call “probability-driven search”:

ai-search-visibility-comparison-perplexity-chatgpt-gemini-copilot

Ahrefs study of 15,000 prompts comparing AI assistants’ overlap with Google search results.

Old SEO	New GEO (Generative Engine Optimization)
Optimize for one keyword	Optimize for passage-level retrieval across dozens of query variations
Rank #1 for that keyword	Increase your probability of being cited in 10–20% of LLM responses
Get consistent traffic	Monitor citation frequency instead of rankings

Key Research Takeaways

Only 12% of LLM citations overlap with Google’s top 10 results (Ahrefs, 15k queries).
80% of citations come from pages that don’t rank for the target keyword.
Citation rotation happens every 24–72 hours, especially for commercial queries.

That last point? That’s exactly what we observed at Wellows.

What Should You Do When Your Citations Disappear

When a citation disappears, most people either panic and overhaul everything or ignore it completely. Both are mistakes.

Unlike traditional backlinks, LLM citations behave probabilistically, fluctuating as retrieval systems update their source weighting—so volatility is part of the system, not a signal of penalty.

The right move is to diagnose first, then act, because every disappearing mention could signal a missed AI citation opportunity. Citation loss has two core causes, and each one needs a different response.

Root Cause 1: You Lost the Third-Party Mention

This is when the source that was citing you a blog post, a comparison article, a roundup either:

Removed your mention
Updated the content and dropped you
Got taken down entirely

What to do: Get it back. Immediately. Why? Because for LLMs, every 3rd party mention is a vote of confidence. RAG systems don’t just look at your owned content they weight external signals heavily. If you were in someone’s “Top 10 AI Marketing Tools” post and now you’re not, that’s not LLM volatility. That’s reputation erosion.

Action Items:

Audit the disappeared citation. Use Wellows to see what changed.
Reach out to the author/site. If it was an update, ask to be re-included. Offer updated info, quotes, or assets.
If it’s gone for good, replace it. Find 2-3 similar sources and get mentioned there instead.

Remember: You’re not just recovering one citation you’re maintaining your mention density across the corpus LLMs can retrieve from.

Root Cause 2: The LLM Changed Its Sources

This is the probabilistic rotation I described earlier. The 3rd party content is still there, still mentioning you but the LLM chose a different set of sources this time.

What to do: Go after the new citations. Here’s the insight most people miss: If a source is being cited today, it might rotate out tomorrow. But if you can get your brand mentioned on both today’s sources AND tomorrow’s sources, you’re building what I call citation saturation.

The 60-Day Strategy: Over 60 days of disciplined execution, you can get your brand on every possible source in the LLM’s retrieval pool for your category. At that point, the LLM has no choice but to surface you because you’re in the option set no matter which sources it pulls.

Action Items:

Track which new sources replaced you. Don’t just notice you’re gone see who showed up instead.
Get mentioned on those sources. Contribute content, offer expert quotes, sponsor roundups, guest post whatever it takes.
Repeat the cycle. Every time the citations rotate, identify the new sources and get on them.

This is a compounding strategy: Month 1, you’re on 3 sources. Month 2, you’re on 7. Month 3, you’re on 12. By Month 6, you’re in 80% of LLM responses because you’ve saturated the retrieval pool.

What We’re Seeing at Wellows

We built Wellows because of this volatility. Traditional SEO tools track rankings. We track LLM citation presence daily as part of structured AI visibility measurement across OpenAI, Gemini, Claude, and Perplexity.

And because this isn’t useful unless you can act on it, you can export these citation opportunities every day and plug them straight into your outreach, PR, or content workflows.

What we’re seeing in our monitoring data across hundreds of thousands of citations:

Citation rotation happens in 24-72 hour cycles, especially on:

Commercial/transactional queries (“best X for Y”)
Queries influenced by UGC signals (Reddit, forums)
Rapidly evolving topics (AI tools, crypto, breaking news)

Stable citations tend to cluster around:

Well-structured FAQ/comparison content
Pages with explicit entity markup (schema, author bios, citations)
Content that covers semantic neighbors (related sub-topics)

The brands seeing consistent LLM visibility are treating it like a brand awareness play, not a direct-response channel. They’re not trying to “Rank #1” in ChatGPT they’re trying to show up in 10-15% of relevant queries and compound that over time.

And they’re doing exactly what I described: tracking source rotation and systematically getting mentioned on every new source that emerges.

My Take: Always Chase It But Be Smart About How

So here’s my position:

If you lost a 3rd party mention → 100% go get it back.Non-negotiable. Every mention is a signal to LLMs. Don’t let your mention density erode.

If the LLM rotated sources → 100% go after the new sources.This is your opportunity to expand coverage. Get on the new citations. Then do it again next month. And the month after.

The endgame: Over 60-90 days, you saturate the retrieval pool. At that point, LLM volatility works for you instead of against you because no matter which sources it picks, you’re in the option set.

This isn’t “SEO vs. GEO.” It’s SEO and GEO working together. The same strategies that win third-party mentions thought leadership, data, quotes, and tools also create every new AI citation opportunity that fuels long-term LLM visibility.

But you need the right monitoring infrastructure. If you’re manually checking ChatGPT every few days, you’ll miss the rotation cycles. You need to track:

Which queries cite you (and when)
Which sources are being cited alongside or instead of you
Whether your mentions are growing or shrinking across the retrieval pool

That’s the real game now not ranking for one keyword, but building enough presence to saturate the option set so LLMs can’t avoid you.