If you have used ChatGPT, Claude, or any AI chatbot in the past year, you have interacted with a large language model. But what actually is an LLM under the hood? I spent months working with these models before I felt like I truly understood how they work, and I want to explain it in a way that does not require a computer science degree.
What Makes a Language Model Large
A language model is a type of AI that is trained to predict the next word in a sequence. That sounds simple, and the core idea really is that simple. You give it some text, and it guesses what comes next. The difference between a small language model and a large one is scale. Large means two things: a massive amount of training data and a massive number of parameters.
Parameters are the internal settings that the model adjusts during training. Think of them as the knobs and dials that the model tweaks to get better at predicting words. A small model might have a few hundred million parameters. A large model like GPT-4 or Claude has hundreds of billions. That scale is what allows these models to understand context, follow instructions, and generate coherent responses on topics they were never explicitly programmed to handle.
How They Are Trained
Training an LLM is a massive engineering effort. The process has three main stages. First, the model is trained on a huge corpus of text from the internet, books, academic papers, and other sources. This is called pretraining. The model reads billions of sentences and learns patterns, grammar, facts, and reasoning abilities just by trying to predict the next word over and over again.
Second, the model goes through instruction tuning. Human reviewers provide examples of good responses to specific prompts, and the model learns to follow instructions rather than just predict the next word. This is what makes ChatGPT feel like an assistant rather than a text autocomplete.
Third, there is reinforcement learning from human feedback, or RLHF. The model generates multiple responses to the same prompt, humans rank them from best to worst, and the model learns to prefer the responses that humans liked.
The Training Cost Is Staggering
Here is a number that put things in perspective for me. Training GPT-4 reportedly cost somewhere between $100 million and $200 million. That includes the electricity, the thousands of GPUs running for months, the data acquisition, and the human reviewers. Llama 3 from Meta cost tens of millions. Even smaller models like Mistral cost millions to train.
This is why the few companies that can afford to train frontier models keep growing in influence. The barrier to entry is not technology, it is capital. You need hundreds of millions of dollars and access to compute clusters that most organizations simply cannot afford.
What LLMs Are Good At
LLMs excel at tasks that involve language understanding and generation. They can write essays, summarize documents, translate between languages, answer questions, write code, and hold conversations. They can also do things that seem like reasoning, like solving math problems or planning a trip itinerary.
But here is the catch. They are not actually reasoning in the way humans do. They are pattern matching on a massive scale. When you ask an LLM to solve a math problem, it is not performing logical deduction. It is recalling patterns from its training data that look similar to the problem you gave it. This works surprisingly well most of the time, but it also means LLMs fail in ways that humans would not.
Where LLMs Fall Short
The biggest problem with LLMs is hallucination. Because they are trained to predict the most likely next word, they will confidently generate false information if the pattern matching leads them in the wrong direction. They do not know what they do not know.
LLMs also have a limited context window. They can only process a certain amount of text at once. If the conversation gets too long, they forget the beginning. Different models have different limits. GPT-4 can handle roughly 25,000 words in a single conversation, while newer models like Gemini can handle millions. But even the larger windows do not solve the attention problem. The model has trouble focusing on the most important information when there is too much text.
Another weakness is that LLMs are static after training. They know nothing about events that happened after their training cutoff. A model trained in early 2026 does not know about something that happened in May 2026 unless it has access to external tools like web search or RAG.
A Simple Example
I asked GPT-4: What is the capital of France? It correctly says Paris. The model was trained on text that says “the capital of France is Paris,” and when I ask the question, the pattern matching kicks in and gives me the right answer. It looks like the model knows the answer, but really it has seen the phrase so many times in its training data that it is the most statistically likely completion.
Now I ask: What is the capital of a fictional country called Zephyria? The model will likely make something up. It might say Zephyr City or something that sounds plausible. It does not know that Zephyria does not exist. It just knows that when someone asks for a capital, the pattern completion says to answer with a city name. This is hallucination in action.
Why Size Matters
Larger models are generally more capable, but the relationship is not linear. Doubling the number of parameters does not double the performance. There are diminishing returns. A 70 billion parameter model is much better than a 7 billion parameter model, but a 700 billion parameter model is only slightly better than the 70 billion one. The gains shrink as the models get bigger, but the cost keeps rising.
This is why the industry is moving toward smaller, specialized models for specific tasks. Why pay for a 700 billion parameter model to summarize an email when a 7 billion parameter model can do it just as well for a fraction of the cost?
My Perspective
LLMs are one of the most impressive technologies I have ever worked with, but they are not magic. They are statistical text predictors trained at an enormous scale. Understanding what they actually are helps you use them better. You stop expecting them to be perfect and start designing systems that account for their weaknesses. This is why techniques like RAG, prompt engineering, and tool use exist. They are all ways of making LLMs more reliable despite their fundamental limitations.
The key takeaway is this. LLMs are incredibly powerful pattern matchers, not reasoning engines. Treat them as such, build guardrails around their weaknesses, and they will transform how you work. Expect them to think like a human, and you will be disappointed every time.
Learn more about RAG Explained: How AI Gets Its Facts Straight.
Related: how AI tools actually work.
