Understanding Large Language Models in Plain English (No PhD Required) Meta Description: What is a large language model and how do they work? A simple explanation of tools like ChatGPT, Claude, and Gemini explained.
Try asking an everyday ChatGPT user how it works. Most will pause, laugh, and say something along the lines of “it’s just… smart, I guess?”
That’s understandable! It’s like asking anyone how their car works; you don’t need to know, but it doesn’t hurt to understand. In the case of language models, being able to understand how it works makes using it that much better. So let’s open up the hood and take a peek, shall we?
What “Large Language Model” Actually Means
Let’s dissect the phrase, word by word:
– Language: Has something to do with language
– Model: Is a fancy word for “mathematical system”
– Large: Probably has something to do with being large in scale
And now, let’s put those words together: A large language model is a mathematical system trained on a huge amount of text, which gives it an ability to recognize patterns in the language, and use that to predict what words come next in a way that’s coherent and useful to humans.
The Magic Behind the Curtain: “Next Word Prediction”
But wait, isn’t that just a fancy way of saying it guesses the next word? Essentially, yes. The “magic” behind large language models is that it’s making highly accurate next-word predictions, only it’s doing so at a scale never before seen, and with nuance.
If you’ve ever used a smartphone to text someone, you may be familiar with how the phone will suggest the next word. Essentially, an LLM is doing the same thing, only it’s doing this at a much larger scale, and with more nuance. This is what allows LLMs to write essays, explain concepts, and even appear to have a personality.
How Did It Learn All This?
When the model was being trained, it was shown a large body of text, and it had to guess what word comes next. Whenever it got it right, the system told it to continue, and made slight adjustments to the algorithm so the model could get it right more often. By doing this over and over and over again, this allows the model to get really good at predicting the next word. This in turn affects everything else, enabling it to write essays, explain concepts, and even pretend to have a personality.
Does It “Understand” What It Writes?
This is actually a heavily debated topic. Essentially, nobody knows for sure whether an LLM “understands” the text it writes. What we do know is that these models do not think or reason in the way humans do; instead, they’re excellent at pattern recognition.
In short: It doesn’t truly “understand” what it writes, but it knows the patterns of language well enough to mimic the writing process.
Why Does It Sometimes Say Nonsense?
This is commonly referred to in the industry as “hallucination”, and it’s a feature, not a bug. Essentially, it’s the model doing what it was designed to do – predict the next word, based on patterns in the text. Due to the way these models are designed, and how they train themselves, there’s a possibility that the model will put out information that is confidently wrong. That is why it’s a good idea to treat these models as a reference or an explainer, but never a source of factual information. Especially when it comes to numbers, dates, and sources, it’s always good to fact-check with a search engine.
Why Does It Sometimes Sound So Human?
LLMs are designed to mimic the way humans write, speak, and communicate. This is in part why the output is so coherent and, to some, “human-like”. This is a design choice that’s meant to improve the user experience, and it works. That doesn’t mean it has a soul, though.
Why Is It Called “Large”?
There’s a reason why these are called “large language models.” Larger data sets and more parameters allow the model to get better at what it does, but it’s not a requirement for it to be effective. There are other factors at play in determining how useful the model is, like the quality of the training data and the tuning process. This is also partially why different AI tools feel different; they might have different data sets, feel like different personalities, and have different strengths and weaknesses.
Final Thoughts
LLMs are what they are because of what they’ve been trained on, and how they’ve been trained. By understanding how these models work, their applications and limitations should become more apparent. By knowing their limitations, you can better understand when to use, and when not to use, an LLM.
In short: a large language model is pattern-recognition system, trained on a large body of text, which allows it to predict the next word in a sentence with enough nuance and accuracy to appear human-like. Once you understand this, these black boxes no longer appear so much like a magic trick, and more like an impressive feat of engineering.
Understanding how LLMs work makes using them easier and more accessible, while also reminding us of their limitations. Knowing these things will make you a more effective user of an AI tool, and that’s a superpower in today’s world.
Your Turn
Now that you know how large language models work, how do you feel about them? Does this enhance your enjoyment of them, or lessen it? Tell us in the comments below!
