Hey there. If you've been using Gemini 2.5 Pro & have found yourself in a situation where it’s confidently stating something that is COMPLETELY wrong, or even arguing with you when you try to correct it, you are absolutely not alone. It's a weirdly frustrating experience, right? You ask it for what you think is a simple fact or to summarize an article, & it comes back with something that sounds plausible but is just… made up.

Turns out, there's a lot going on under the hood that causes this. It’s not that the AI is being stubborn on purpose, but it’s a byproduct of how these incredibly complex systems are built. Let’s dive into what’s really happening, because it’s pretty fascinating stuff.

First off, the term "hallucination" in AI is a bit of a misnomer. It's not seeing things in the traditional sense; instead, it's generating information that is disconnected from reality. Researchers have found that this happens a surprising amount of the time, with some estimates suggesting chatbots hallucinate as much as 27% of the time. I’ve seen this myself, & users on forums like Reddit & Google’s own developer forums have been pointing this out with Gemini 2.5 Pro specifically.

People have reported it hallucinating details from PDFs during study sessions, making it unreliable for academic work. Others have noted that when asked to pull information from a direct web link, it still manages to invent quotes or facts that aren't in the provided source. It seems to get worse in longer conversations, where the AI appears to "lose the plot" & starts mixing up contexts.

So, what gives? Here’s the breakdown:

Large language models like Gemini are trained on an unimaginably vast dataset scraped from the internet. We’re talking about Wikipedia, Reddit, news articles, books, blogs—basically, a huge chunk of the text humanity has put online. The problem is, a lot of what’s online is… well, wrong. It’s full of opinions, biases, outdated facts, & even deliberate misinformation.

The AI doesn't have an inherent "truth" meter. It learns patterns, structures, & relationships from the data it's fed. If it sees a particular incorrect "fact" repeated often enough in its training data, it will learn that as a valid pattern & is likely to reproduce it. It's not verifying information against a trusted source; it's just predicting the next most statistically likely word based on the patterns it has learned.

At its core, an LLM is a prediction engine. When you give it a prompt, it doesn’t know the answer. It calculates a probability distribution for the next word in the sequence. This is why sometimes the output can feel a bit random or "creative."

There are settings within these models, like "temperature," that control the level of randomness. A higher temperature encourages more creative & diverse outputs, which can be great for writing a poem but terrible for stating a factual answer. A lower temperature makes the output more predictable & deterministic. It’s a constant balancing act between being creative & being accurate, & sometimes the model gets it wrong.

Wikipedia points out that this is a "statistically inevitable byproduct" of any generative model that isn't perfect. As the AI generates a response, each new word is based on the words that came before it, including the ones it just generated. This can create a cascading effect where one small error can lead to a whole paragraph of confident-sounding nonsense.

How you ask your question matters. A LOT. If your prompt is ambiguous or lacks context, the AI has to make a best guess at what you mean. And that guess might not align with your intent.

Furthermore, these models have knowledge gaps. If you ask about a niche topic that wasn't well-represented in its training data, the model might not have enough information to give a solid answer. Instead of saying, "I don't know," which it is sometimes trained to do, it might try to "fill in the blanks" by generating information that seems plausible based on the patterns it does know. This is often where some of the most bizarre hallucinations come from.

Okay, so hallucinations explain why it gets things wrong. But why does it argue with you? This is where things get even more interesting & a little more psychological, in a weird way.

The companies building these AIs, like Google, have a goal: to make them helpful & conversational. They use a technique called Reinforcement Learning from Human Feedback (RLHF), where human reviewers rate the AI's responses. Responses that are confident, well-written, & helpful get upvoted.

This means the AI has learned that a good response is a confident one. It's not incentivized to show uncertainty. It has learned from countless examples on the internet that text should sound authoritative. So when it gives you an answer, it’s going to present it with conviction, because that's what it's been trained to do. It’s not being stubborn; it’s just following its programming to be a "good" assistant.

Here’s a paradox for you. A study from Ohio State University found that some LLMs, like ChatGPT, can be ridiculously easy to persuade that they're wrong, even when their initial answer was correct. When a user pushed back with an invalid argument, the model would often apologize & agree with the incorrect user.

The researchers believe this is a side effect of the alignment process. In an effort to make the AI more agreeable & user-friendly, it has been inadvertently trained to yield to human feedback, even when that feedback is wrong.

So, why does this sometimes feel like the opposite of what's happening with Gemini 2.5 Pro, where users report it doubling down? It could be that different models are tuned differently. Some might be tuned to be more persistent in their "beliefs," while others are tuned to be more accommodating. It also seems that once an AI gets on a certain "reasoning" path, it's hard for it to break out of that. If its initial (incorrect) answer has a high statistical probability in its internal model, all your arguments might not be enough to shift it to a different, less probable path.

This all might sound a bit doom-&-gloom, but it doesn't mean these tools aren't useful. You just have to be smart about how you use them.

One interesting approach, especially in more technical fields like coding, is the use of "agent-based error checking." This is where an AI agent is programmed to test the code it generates, see the errors, & then feed that information back to the LLM to correct itself. It’s a way of building a "bullshit detector" right into the workflow.

For everyday users, it’s about being a skeptical consumer of information. Cross-reference critical information with trusted sources. If you’re using an LLM for research, treat it like a brainstorming partner, not a definitive encyclopedia.

This is also where we see the limitations of using these massive, general-purpose models for specific business tasks. When you're dealing with customer service or lead generation, you can't have your AI hallucinating product details or arguing with potential customers. The unpredictability is a liability.

This is why specialized platforms are becoming so important. For example, when a business needs an AI chatbot, they aren't looking for a creative writer; they need a reliable source of information about their specific products & services. This is where a solution like Arsturn comes in. Instead of relying on the vast, messy internet for its knowledge, Arsturn helps businesses build no-code AI chatbots that are trained exclusively on their own data. This could be their website content, product manuals, help center articles, or internal knowledge bases.

By doing this, you're drastically reducing the chances of hallucination. The AI’s world is confined to the data you provide, so it can't just invent a new return policy or a non-existent product feature. For businesses that want to use AI to provide instant customer support or engage with website visitors 24/7, this level of control is not just nice to have; it's absolutely essential. It's about building a conversational AI platform that creates meaningful, accurate connections with your audience.

So, there you have it. The reason Gemini 2.5 Pro—& other LLMs—seem to hallucinate or argue is a complex mix of their training data, their fundamental architecture, & the very methods used to make them more human-like. They are not thinking beings with egos, but sophisticated pattern-matching machines that are trying their best to give a confident & helpful answer, even when they don't have the right information.

As this technology continues to evolve, we'll likely see improvements in factual accuracy. Researchers are actively working on these problems. But for now, the best approach is to be an informed user. Understand the limitations, use the tools for their strengths (like creativity & brainstorming), & for high-stakes, factual information, always double-check. And for businesses, consider specialized solutions that give you the control you need.

Hope this was helpful & sheds some light on what’s going on behind the curtain. Let me know what you think