How Large Language Models Work in Plain English (Tokens, Training, Attention)

Your phone guesses the next word as you type. Sometimes it nails it, sometimes it suggests something odd, but the basic trick is the same every time: it predicts what comes next.

A large language model (LLM) is that idea scaled up, trained on vast amounts of text, and powerful enough to write emails, summarise reports, translate languages, and help with code. It can also sound completely sure while being wrong, which is why understanding the basics matters.

This guide explains LLMs without maths: tokens (the pieces of text they use), next-token prediction (how they generate replies), transformers and attention (how they keep context), training (how they learn patterns), and why “smart-sounding” doesn’t always mean “true”.

What is a large language model, really?

A large language model is a computer program trained on lots of text that learns patterns in language. When you give it a prompt, it doesn’t “think” like a person or look up facts like a search engine. It makes probability-based guesses about what text should come next.

That might sound limited, but it’s surprisingly useful. If you can predict the next token well enough, you can:

• Draft and rewrite text in different tones
• Summarise long documents
• Translate between languages
• Explain concepts at different reading levels
• Suggest code, tests, or refactors

What LLMs aren’t great at:

• Guaranteed truth (they can invent details)
• Perfect maths (they can slip on multi-step arithmetic)
• Real-world awareness (they don’t “see” what’s happening around them)
• Up-to-the-minute events (unless connected to external tools)

If you want a deeper, still readable explanation, Miguel Grinberg’s walkthrough is a strong companion: How LLMs Work, Explained Without Math.

Tokens, not words: how text is chopped up

LLMs don’t read text as “words” the way we do. They use tokens, which are chunks of text. A token can be a whole word, part of a word, punctuation, or even a space.

A simple way to picture it is LEGO bricks: the model builds sentences from small pieces. The pieces aren’t always neat, which is why names, slang, and unusual spellings can behave oddly.

Here’s what tokenisation can look like (exact splits vary by model):

Tokenisation matters because it affects:

• Cost and speed: more tokens means more work
• Context length: models can only “see” a limited number of tokens at once
• Weird edge cases: rare names, mixed languages, or lots of symbols may split into many tokens

Next-token prediction: the simple idea behind the magic

At the centre of an LLM is one job: predict the next token.

Give it: “The cat sat on the” It might predict: “ mat”

Then it takes the whole new string, “The cat sat on the mat”, and predicts the next token again, repeating until it decides to stop.

A key point: there usually isn’t one “correct” next token. Language allows options.

Try this: “The meeting is scheduled for”

Likely next tokens include “ Monday”, “ tomorrow”, “ next”, “ 3pm”, depending on context. The model picks from a list of probabilities, and the settings can change how it picks.

Two common controls:

• Temperature: how risky or creative the guesses are (higher means more variety)
• Top-p (nucleus sampling): only pick from the smallest set of likely tokens that add up to a chosen probability, to avoid very unlikely text

That’s why the same prompt can produce different answers. The model is sampling from “plausible continuations”, not retrieving one fixed response.

For a visual, interactive way to see this process, the Transformer Explainer is genuinely helpful.

How transformers and attention help an LLM understand context

As of January 2026, the core engine behind most popular LLMs is still the transformer. No fundamental change has replaced it. Improvements tend to be about scale, speed, and efficiency, not a new basic idea.

Transformers help because they let the model look at many parts of your prompt at once. Older approaches struggled when the important clue was far back in the text. Transformers handle that by using a mechanism called attention, which decides what to focus on.

Photo by Google DeepMind

If you want a structured learning path that stays approachable, DeepLearning.AI’s short course page is a good reference point: How Transformer LLMs Work.

Attention: how the model decides what to focus on

When you answer a question in a long email thread, you don’t re-read every line with equal focus. You scan for what matters, like names, dates, and the last decision.

Attention works a bit like that. Technically, it’s the model assigning weights between tokens, deciding which earlier tokens should influence the next-token prediction most.

This helps with:

• Linking pronouns to the right thing (“it”, “they”, “this”)
• Keeping track of who did what in a paragraph
• Staying on-topic when the prompt is long
• Handling long sentences where the key detail appears late

Important caveat: attention isn’t “understanding” in a human way. It’s a learned method for connecting pieces of text so the output stays coherent.

Hugging Face’s learning material explains transformers in a clear, practical style: How do Transformers work?.

Parameters and layers: where the learning is stored

During training, the model adjusts internal values called parameters. You can think of them as millions or billions of tiny knobs that affect how strongly one token influences another.

Those parameters are arranged across layers. Each layer learns different kinds of patterns. Early layers often capture basic structure, later layers capture more abstract relationships.

Bigger models (more parameters, more layers) can learn richer patterns, but bigger isn’t always better. Results depend on:

• The quality and mix of training data
• How training is done (and for how long)
• Safety and instruction tuning
• Whether the model can use external tools (like search or databases)

How LLMs are trained (and why that affects what they say)

LLMs learn by example. They don’t start with rules of grammar or a built-in fact book. They start mostly random, then gradually improve by training on huge amounts of text and adjusting those parameters to reduce prediction errors.

Training takes heavy computing power (often many GPUs running for weeks). That’s not the interesting part for most readers, though. The useful takeaway is this:

The model reflects its training. If the data has gaps, bias, or repeated myths, you can see echoes of that in the answers.

Training is often described in two main stages: pretraining and fine-tuning (alignment).

A readable overview of the full pipeline, from a developer angle, is here: How Large Language Models (LLMs) Work.

Pretraining: learning language by predicting what comes next

Pretraining is the grindstone. The model reads huge amounts of text (books, websites, articles, code, and more), repeatedly trying to predict the next token.

Over time it learns:

• Grammar and spelling patterns
• Common writing styles (formal, casual, persuasive)
• Common facts that appear often in text
• How instructions usually look and how answers usually look
• Patterns in code (syntax, libraries, common fixes)

What it doesn’t learn in a guaranteed way:

• Truth checking (it learns what’s said often, not what’s correct)
• Live updates (it won’t know new events unless connected to tools)
• A consistent “world model” like a person’s lived experience

Fine-tuning and alignment: teaching it to follow instructions safely

After pretraining, many models go through fine-tuning so they behave more like a helpful assistant.

In plain terms, this stage rewards outputs that people rate as helpful, safe, and on-topic, and discourages outputs that are harmful, irrelevant, or unsafe. This often includes human feedback and safety rules.

Alignment makes the model easier to use in chat and less likely to produce harmful content, but it doesn’t make it perfect. It can still:

• Misread your intent
• Guess when it should ask a question
• Refuse a safe request by mistake
• Produce a confident answer that’s wrong

Why LLMs can be helpful, and why they can be confidently wrong

LLMs are brilliant for language tasks where “good enough” is valuable: getting started on a draft, turning notes into a summary, or generating options you can pick from.

They struggle when you need ground truth, like legal citations, medical decisions, or financial figures. The same fluent engine that makes them pleasant to read can also make mistakes feel believable.

A practical way to use an LLM is to treat it like a fast assistant who’s great at phrasing, structure, and brainstorming, but who sometimes fills gaps with guesswork.

Hallucinations: fluent text is not the same as truth

A hallucination is a fluent guess that isn’t grounded in facts. It can look like:

• Made-up references that sound real
• Wrong dates, names, or numbers
• Confident explanations for something it doesn’t know
• “Quoted” policies that were never published

Common triggers include vague prompts, missing context, edge cases, and requests for exact citations.

Ways to reduce hallucinations in day-to-day use:

• Ask for sources and check them yourself
• Tell it to separate facts from assumptions
• Provide key context (names, dates, constraints) in the prompt
• Request uncertainty when needed (“say if you’re not sure”)
• Test with a small prompt first, then expand

The goal isn’t to “trust it less”, it’s to use it where it’s strong, and verify where it’s weak.

Context windows and memory: what it remembers, and what it forgets

An LLM has a context window, which is the amount of text it can consider at once. If the conversation gets too long, earlier details may fall out of view.

That can surprise people because chat feels like a continuous relationship. In reality, the model works with the text it can see right now. Some tools summarise older parts of a chat to help, but that summary can lose detail.

Tips that work well:

• Paste important facts again before asking a key question
• Put constraints in a short list at the end of your prompt
• If you’re iterating on a document, re-share the latest version
• Ask it to restate requirements before it answers (a quick self-check)

Conclusion

Large language models work by turning text into tokens, using transformers with attention to weigh context, then generating replies by predicting the next token, again and again, based on patterns learned during training. That core hasn’t changed as we enter 2026, even as models get faster and more efficient.

Use LLMs as writing and thinking helpers, not as a final judge of truth. When the stakes are high, verify the details. A simple next step: try a prompt that asks for an answer plus a short “how I got this” explanation, then see where it’s solid, and where it starts guessing.