A plain-language guide
The simplest way to understand an LLM is to look at the autocomplete feature on a smartphone. When you type How are, it suggests you. It does this because it has seen that sequence of words millions of times.
An LLM is that same technology, but instead of looking at the last two words, it looks at billions of pages of text — books, websites, articles, and more.
Imagine a machine that has read every book in the world's largest library. It didn't "learn" the facts in the books; it learned the patterns of how humans use language. It knows that when someone asks a question about baking, words like "flour," "oven," and "temperature" usually follow.
When you send a message, the LLM isn't "thinking." It is calculating a sequence of probabilities.
If you provide the prompt "The sky is", the AI looks at its internal map and sees something like this:
It picks "Blue" and then starts the process over. Now it looks at "The sky is blue" and calculates the next most likely word — perhaps a period, or the word "today". Every single word is a fresh probability calculation, feeding on all the words that came before it.
You might think the AI "remembers" you or has a personality. It doesn't.
Every time you hit Enter, the AI starts from scratch. It has no internal notebook, no stored recollection of who you are.
To make it feel like a conversation, the software quietly sends the entire previous conversation back to the AI with every new message. The AI isn't "remembering" the last five minutes — it is re-reading the transcript of those five minutes every single time you reply.
Since the LLM is a pattern-matcher, the quality of the "pattern" you give it determines the quality of the output. This is why "garbage in, garbage out" is the golden rule.
If you ask for medical advice, it might give a generic answer. If you tell it, "Act as a world-class cardiologist explaining a concept to a 10-year-old," you force the AI to look at a very specific subset of its "library" — medical texts combined with children's books.
A vague prompt like "Write a letter" is hard for the AI because "letter" has billions of possible word paths. But "Write a formal letter to a landlord requesting a sink repair" narrows the probability map, making the "correct" words much more likely to appear.
If you ask a complex question, tell the AI to "Think step-by-step." This forces it to generate tokens for the process of solving the problem, which then serve as the context for the final answer. It's like asking someone to show their math rather than just blurt out a number.
Because it is a probability engine, not a database, it will prioritize sounding correct over being factual. If a "fact" doesn't exist in its training data, it will predict the most likely-sounding fake fact. You should know that the AI is a poet, not an encyclopedia. It is designed to be fluent — not necessarily truthful.