AI Performance Fluctuation: Why LLMs Have Good & Bad Days

8/13/2025

Why Your AI's Performance Seems to Fluctuate Day-to-Day

Ever feel like you're going crazy? One day, your favorite AI model is a genius, a coding partner, a creative muse that just gets you. The next, it feels like it’s had a lobotomy. It’s forgetful, makes silly mistakes, & gives you generic, uninspired answers to the same prompts that yielded gold just yesterday.

If this sounds familiar, I’m here to tell you: you’re not imagining it.

The performance of large language models (LLMs) like the ones powering ChatGPT, Claude, & others can & does fluctuate. It’s a weird, frustrating phenomenon that has developers, writers, & casual users alike scratching their heads & heading to forums to ask, "Is it just me?"

Spoiler: It’s not just you.

Here’s the thing, the reasons for this fluctuation are a complex mix of technical quirks, business realities, & even a little bit of human psychology. As someone who spends a LOT of time in this space, I’ve been down the rabbit hole on this topic. Turns out, there’s a lot going on under the hood. So, let's pull back the curtain & get into why your AI seems to have good days & bad days.

The Core of the "Problem": These Things are Inherently Unpredictable

First, we have to wrap our heads around a fundamental concept: LLMs are not like traditional software. They are not deterministic. You can't input '2+2' & expect '4' every single time. It's more like a conversation with an incredibly knowledgeable, but sometimes moody, person.

This is what some researchers call "Intrinsic Variability." A study highlighted in a Medium article pointed out a wild example: in March 2023, GPT-4 had a 97.6% accuracy rate in identifying prime numbers. By June of the same year, that accuracy plummeted to a shocking 2.4%. That’s not a small change; that's a complete flip. The same model, a few months apart, with VASTLY different performance on a specific task.

This variability is baked into their DNA. LLMs are built on neural networks & probabilities. When you give it a prompt, it's not searching a database for a single right answer. It's predicting the next word, & the next, & the next, based on a complex web of patterns it learned from its training data. A tiny change in the starting conditions or the model's internal state can send the response down a completely different path.

Think of it like the butterfly effect. A butterfly flaps its wings in Brazil, & you get a tornado in Texas. In an LLM, a tiny nudge in its probabilistic calculations can be the difference between a brilliant essay & a paragraph of gobbledygook. Researchers have also found that this "intra-model variability" means the same LLM, with the same prompt, can give you answers that range from below-average to truly original & insightful.

So, part of what you're experiencing is just the nature of the beast. It’s not a bug, but an inherent feature of how these models work. It’s both what makes them so creative & so maddeningly inconsistent.

"Context Rot": When More is Less

This is a BIG one, & it’s a bit more technical, but it’s crucial to understanding performance issues. A fascinating report from Chroma Research titled "Context Rot" dug deep into this. We tend to assume that LLMs can handle massive amounts of text—we see announcements of models with million-token context windows & think we can just dump a novel in there & ask questions.

Turns out, that's not how it works. The Chroma report found that model performance degrades as the input length increases. Even on simple tasks, the more context you give the model, the more unreliable it becomes. It’s like asking a person to remember a single, specific detail from a ten-hour-long meeting. The longer the meeting, the harder it is to recall that one little nugget of information.

The researchers at Chroma found this across 18 different LLMs, including the big names like GPT-4.1 & Claude 4. They called this "Context Rot." The model doesn't use its context window uniformly. The 10,000th token is NOT treated with the same importance as the 100th.

Here are some of the wild things they discovered:

Ambiguity is a killer: The more ambiguous the task, the worse the performance degradation with longer context. If the answer isn't a direct copy-paste from the text, the model struggles more as the text gets longer.
Distractors are a HUGE problem: If you put information in the context that is similar to the correct answer but isn't quite right (a "distractor"), the model can get easily confused. The more distractors, & the longer the context, the worse the performance. Some models, like Claude, tend to just give up & say they can't find the answer, while GPT models are more likely to hallucinate a confident but wrong answer.
The structure of the context matters: This is where it gets really weird. The Chroma team found that models performed better on shuffled, less coherent text than on well-structured, logical text. It's counter-intuitive, but it seems that a perfectly structured essay might make it harder for the model to pick out a single, out-of-place "needle" of information.

So, if you're working with long documents, complex instructions, or a long chat history, you are likely a victim of context rot. The model isn't necessarily "dumber" today than it was yesterday; you might just be pushing its memory & attention to its limits.

The Business of AI: Cost, Speed, & Silent Updates

This is where we get into the less technical & more… well, corporate side of things. Running these massive models is INSANELY expensive. We're talking about massive server farms filled with cutting-edge GPUs that eat electricity for breakfast. Companies like OpenAI are in a constant battle to make this sustainable.

This leads to a fundamental trade-off: cost vs. performance.

You’ll see this all over developer forums & Hacker News threads. A common complaint is that a new version of ChatGPT will suddenly get much faster, but the quality of the responses takes a nosedive. The running theory, & it’s a pretty solid one, is that the company is using a "smaller" or more "quantized" model. This is a version of the model that has been optimized to run faster & use less computing power. The trade-off? It’s often less precise, less creative, & a little bit "dumber."

One user on the OpenAI forums put it perfectly: "It feels like I'm using a slightly smarter version of GPT 3.5." This is a direct consequence of the business realities of providing AI to millions of users. It’s not always about giving you the absolute best, most powerful model; sometimes, it’s about giving you a "good enough" model that doesn’t bankrupt the company.

Then there's the issue of silent updates & A/B testing. You might be using a slightly different version of the model today than you were yesterday, & you'd have no way of knowing. Companies are constantly tweaking things:

Safety Filters: They are always updating their safety filters to prevent misuse. Sometimes these filters can be a bit overzealous & make the model more "prudish" or unwilling to engage with certain topics, even if they are harmless.
Hidden Prompts: The ChatGPT interface has a hidden "meta prompt" that guides the AI's behavior. This can be changed at any time, which could explain why the same model feels different through the chat interface compared to the API.
A/B Testing: You could be part of a group of users testing a new feature or a different model configuration without your knowledge. This is a standard practice in software development, but with LLMs, it can lead to that feeling of "why is this suddenly so different?"

Many users in these forums have noted that the API version of a model often feels more stable & powerful than the version you get through the free (or even paid) chat interface. That’s because the chat interface has all these extra layers of stuff going on that can affect performance.

The Human Element: Are We Part of the "Problem"?

Finally, we have to look in the mirror. Our own perception & how we interact with these models can also play a role in how we perceive their performance.

The Novelty Wears Off: When you first start using a powerful LLM, it feels like magic. You're blown away by what it can do, & you're more likely to overlook its flaws. As you get more used to it, the "wow" factor fades, & the mistakes become more glaring. This is a common theory floated on Hacker News—that the model hasn't gotten worse, our expectations have just gotten higher.
Prompting is a Skill: Crafting the perfect prompt is an art form. As a model evolves, the prompts that used to work perfectly might need to be tweaked. The Dev-kit article on maximizing LLM performance goes deep into this, explaining that clear instructions, decomposing complex tasks, & giving the model time to "think" can dramatically improve results. If you're using the same prompts you were six months ago, they might not be as effective on the current version of the model.

So, What Can You Do About It? The Quest for Consistency

Honestly, if you're a casual user of the big, public models, you're mostly at the mercy of the companies that run them. You can't stop them from A/B testing or rolling out updates. But you can be more mindful of the factors you can control. Keep your prompts clear, be aware of context rot, & maybe try a few different phrasings if you’re not getting the results you want.

But for businesses, this level of unpredictability is a MAJOR headache. If you're building a customer service bot, a lead generation tool, or any kind of automated workflow that relies on an LLM, you can't have it performing brilliantly one day & falling flat on its face the next. You need reliability & consistency.

This is where the idea of a more controlled AI environment becomes SUPER important. Here's the thing, instead of relying on a general-purpose model that’s constantly changing, businesses are finding more success with custom-trained AI.

For example, with a platform like Arsturn, a business can build its own AI chatbot trained specifically on its own data—its website content, its product documentation, its help articles, etc. This creates a much more predictable & consistent experience. The AI isn't trying to be a poet or a coder; its one & only job is to answer questions about that specific business.

When you're dealing with customer service, this is a game-changer. You don't want your support bot to suddenly get "creative" with its answers or forget a key policy because of a silent update. By using a platform like Arsturn, you're building a no-code AI chatbot that provides instant, reliable support 24/7. It gives your customers the right answers, every time, because it's working from a stable, known set of information—your information. This approach sidesteps a lot of the problems with performance fluctuation because you're not subject to the whims of a massive, general-purpose model that’s being tweaked for a million different use cases. You get an AI that's optimized for YOUR specific needs.

Wrapping it Up

So, there you have it. The day-to-day fluctuation in your AI's performance isn't just in your head. It's a real thing, caused by a combination of the model's inherent weirdness, the physical & computational limits of its context window, the business realities of running a massive AI, & our own changing expectations.

It’s a strange new world, & we're all still learning the quirks of our new AI companions. For now, the best we can do is understand what's going on behind the scenes, adapt our prompting strategies, & for businesses that need that rock-solid consistency, look towards custom solutions that put them back in control.

Hope this was helpful & cleared some things up! Let me know what you think. Have you experienced this AI personality split? Found any tricks that help? I'd love to hear about it.