Understanding ChatGPT and LLMs

What Is a Large Language Model?

ChatGPT, Claude, Gemini, Llama — they all belong to the same family of AI called large language models, or LLMs. The name tells you a lot: they are large (trained on an enormous portion of text from the internet), they work with language, and they are models — mathematical representations of patterns in that text.

The single most important thing to understand about how they work: LLMs predict the next most likely word (or token) given everything before it. They do this billions of times per response. The result feels like understanding, but it is pattern completion operating at a scale that produces remarkably coherent output.

Training: Where Does the Knowledge Come From?

Before an LLM can talk to you, it is trained on a huge corpus of text — books, websites, code, academic papers, and more. During training, the model adjusts billions of internal parameters until it becomes very good at predicting how text should continue. This process takes weeks, enormous computing power, and costs millions of dollars.

After training, the model is frozen — it does not learn from your conversations in real time. This is why ChatGPT has a knowledge cut-off date. Anything that happened after that date simply was not in the training data.

What Is a "Token"?

LLMs do not process letters or whole words — they process tokens, which are chunks of text usually 3–4 characters long. The word "learning" might be one token. "Unbelievable" might be two. This detail matters when you are working with very long inputs, because models have a context window — a limit on how much text they can hold in their working memory at once.

Why Do They Sometimes Get Things Wrong?

Because LLMs are predicting likely text rather than retrieving verified facts, they can produce confident-sounding statements that are completely false. This is called hallucination. It happens most often when you ask about obscure topics, recent events, or specific statistics.

The fix is not to distrust AI entirely — it is to verify claims that matter, just as you would fact-check an article you read online.

ChatGPT vs Claude vs Gemini — Are They Different?

All three are LLMs, but they are trained differently, on different data, with different fine-tuning. In practice this means they have different strengths, writing styles, and safety guardrails. None is universally best. Many power users keep two or three open at once and choose based on the task.

What they share matters more than their differences: the same basic approach of text-in, text-out, the same tendency to hallucinate occasionally, and the same core skill of working with human language at speed and scale.