What is an LLM?

AI Engineering

What is an LLM? A plain-English guide for non-developers

Informatics Hub June 2025 6 min read

You've probably heard "LLM" thrown around constantly lately — in tech news, in job listings, and in casual conversation. ChatGPT is an LLM. Claude is an LLM. Gemini is an LLM. But what actually is one, and why does it matter?

This guide explains what a large language model is, how it works at a basic level, and why it's changing everything — without requiring a computer science degree to follow along.

Start with the name

LLM stands for Large Language Model. The three words actually tell you a lot:

• Large — trained on enormous amounts of text, often hundreds of billions of words, using massive computing power
• Language — designed specifically to understand and generate human language
• Model — a mathematical system that has learned patterns from all that data

So an LLM is essentially a very large, very sophisticated pattern-recognition system that has spent its "life" reading a significant portion of the internet, books, articles, and code.

LLMs process language by recognizing deep patterns across billions of examples

How does it actually work?

At its core, an LLM is trained to do one thing: predict what word — or "token" — comes next in a sequence. That's it. But when you train a system to do this on enough data, something interesting emerges: it develops what looks like comprehension, reasoning, and even creativity.

Think of it this way. If someone says "The capital of France is ___", you can fill in the blank without thinking. You've seen that pattern so many times it's automatic. An LLM works the same way — except it has encountered trillions of those patterns, across every topic imaginable.

An LLM doesn't know facts the way humans know facts. It knows what words tend to follow other words — and at massive scale, that turns out to be remarkably useful.

What happens when you type a prompt?

When you send a message to an AI like ChatGPT or Claude, here's roughly what happens behind the scenes:

• Your text is broken into small pieces called tokens — roughly a word or part of a word each
• The model processes those tokens through dozens (or hundreds) of layers of computation
• At each layer, it builds a richer understanding of the context and relationships between words
• Finally, it generates a response — one token at a time — based on what it calculates is most appropriate

This is why AI responses appear to stream word by word. The model is literally generating one token at a time and outputting each one as it goes.

What can LLMs actually do?

The practical uses are wider than most people realize. A well-prompted LLM can:

• Write and edit text — emails, reports, blog posts, social media
• Summarize long documents into digestible highlights
• Answer questions based on information you provide
• Write, explain, and debug code
• Translate between languages with strong accuracy
• Classify and organize content automatically
• Power customer support chatbots
• Extract structured data from messy unstructured text

LLMs are already embedded in the tools millions of people use every day

What can't they do?

Understanding the limits is just as important as knowing the capabilities. LLMs:

• Don't have real-time knowledge unless connected to a search tool
• Can confidently produce incorrect information — this is called "hallucination"
• Don't truly "understand" in the way humans do — they predict based on patterns
• Have no memory between conversations unless specifically built to have it

A useful mental model

Think of an LLM as an extremely well-read assistant who has read almost everything ever written, but hasn't lived any of it. It can discuss surgery in remarkable detail — but you'd still want a surgeon in the operating room.

Why does this matter to you?

Whether you're a developer, a business owner, or someone just trying to keep up with where the world is going — LLMs are increasingly embedded in the tools you already use. Understanding what they are, and what they aren't, helps you use them effectively and avoid the most common mistake: either over-trusting them completely or dismissing them as a gimmick.

In upcoming posts on Informatics Hub, we'll go deeper into how LLMs are being built into real products, how to write effective prompts, and what the growing field of AI engineering actually involves.

Key takeaways

• LLM stands for Large Language Model — trained to predict language patterns at massive scale
• It generates responses one token at a time, not by "looking things up"
• Powerful for text-based tasks, but has real limitations including hallucination
• Already embedded in tools you use every day — understanding them makes you better at using them