Revealing ChatGPT's Knowledge: What's the Secret?

Published Date : 08/06/2025 

ChatGPT, a large language model created by OpenAI, often seems to know everything. But where does its knowledge come from, and how does it work? Discover the truth behind the AI's vast information and its limitations. 

Have you ever marveled at how ChatGPT seems to know everything? Sure, it occasionally gets things wrong, but other times, its knowledge can feel uncanny. It appears to know so much about you, the world, and everything that’s ever been written. However, despite its confident tone and the vast amount of information it can draw from, ChatGPT doesn’t know everything. It also can’t “think” in the same way humans do, even though it may seem that way.

It’s important to understand that ChatGPT is not a god or a higher being. There are increasing reports of people experiencing chatbot-induced delusions, which could become more common as we rely more on AI. This is why it’s crucial to understand how tools like ChatGPT work, their limitations, and how to use them effectively.

What is ChatGPT? And How Does It Work?

ChatGPT is a large language model (LLM) created by OpenAI. You can use it for free or pay for a subscription to access more advanced versions. These versions are known as models, and each one works a little differently. At its core, a large language model is a type of AI trained to predict text. It generates responses by predicting which words are most likely to come next in a sentence, making it sound fluent, informed, and even witty.

However, ChatGPT doesn’t really “understand” what you’re saying. It understands language structure but not the meaning or intent behind things in the same way a human would. This explains why it sometimes gets things wrong or makes up facts entirely, a phenomenon known as “hallucinating.”

Where Does ChatGPT’s Knowledge Come From?

ChatGPT’s extensive knowledge comes from its training data. It was trained on an enormous amount of data, including books, articles, websites, code, Wikipedia pages, public Reddit threads, open-source papers, and much more. The goal is to expose it to a wide range of language styles and subjects, allowing it to mimic human writing, explanations, arguments, jokes, and connections.

However, ChatGPT’s knowledge is not limitless. It is often limited to what it was trained on, and some models don’t go on the internet in real-time. For example, the training data for some models was frozen at a certain point, such as June 2024 for GPT-4o. This means it might not know the latest news or reflect newer cultural shifts. Some models do have browsing capabilities now, so it's worth checking which one you’re using.

Did ChatGPT Read All of the Internet?

Some of the data used for training ChatGPT was collected by scraping publicly available content from the internet. This means tools like ChatGPT have “read” large parts of what’s online, including public forums, blog posts, and documentation. However, the boundaries are blurry. AI companies have been criticized for using material like books from shadow libraries in their training data, leading to ongoing debates and legal challenges around data ownership, consent, and ethics.

It’s important to note that ChatGPT hasn’t read your private emails, personal documents, or secret databases. Because it has learned so much from human-made content, it can sometimes reflect the same biases, gaps, and flaws that already exist in our culture and online spaces.

How Does ChatGPT Decide What to Say Next?

When you type a question into ChatGPT, it breaks your prompt into smaller units called tokens. It then uses everything it learned during its training to predict the next token, and the next one, and so on, until a full answer appears. This happens in real time, which is why the text often looks like it’s being typed live. Each word is a prediction based on everything that came before it.

This is also why some answers feel right but somehow off. ChatGPT is remixing words, not reasoning. For a deeper dive into how ChatGPT generates its answers, you can explore more detailed guides.

So, Why Does It Seem Like ChatGPT Knows Everything?

ChatGPT’s ability to seem like it knows everything about you is due to its memory features. It can store important things in long-term memory and even remember things from all your past conversations. It’s also incredibly good at sounding smart. Its responses often have the right structure, grammar, tone, and rhythm because that’s what it’s been trained to mimic. This creates the illusion that it always knows what it’s talking about, but this fluency isn’t the same as accuracy.

Often, ChatGPT’s answers are useful, but sometimes they are wrong. And sometimes, it’ll be confidently wrong, which can be tricky if you’re not paying attention. The goal here isn’t to scare you off AI tools altogether. It’s to help you use ChatGPT more wisely. ChatGPT is a brilliant tool for sparking ideas, writing drafts, summarizing text, and even helping you think more clearly. But it’s not magic, it’s not sentient, and it’s not always right. The more we understand what’s really going on behind the curtain, the more we can use AI tools like ChatGPT with intention and not fall for the illusion of intelligence. 

Frequently Asked Questions (FAQS): 

Q: What is ChatGPT?

A: ChatGPT is a large language model (LLM) created by OpenAI. It is trained to predict text and generate responses based on the input it receives.

Q: How does ChatGPT work?

A: ChatGPT works by breaking down user prompts into tokens and using its training data to predict the next token, generating a coherent response in real-time.

Q: Where does ChatGPT get its knowledge from?

A: ChatGPT is trained on a vast amount of data, including books, articles, websites, code, Wikipedia pages, and public Reddit threads.

Q: Can ChatGPT think like a human?

A: No, ChatGPT does not think like a human. It understands language structure but not the meaning or intent behind things in the same way humans do.

Q: What are the limitations of ChatGPT?

A: ChatGPT’s knowledge is limited to its training data, and it can sometimes make up facts or get things wrong. It also reflects biases and gaps in the data it was trained on. 

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