Large Language Models Explained: A Plain-English Guide for Non-Developers

Kehinde Adegbesan22 min read
Abstract illustration of a neural network and language patterns

Large Language Models Explained: A Plain-English Guide for Non-Developers

Every time you ask ChatGPT a question, have Claude write a draft, or see a Google result with an AI summary at the top, you're interacting with a large language model.

LLMs are the technology that made the current AI wave possible. They're the engine under the hood of every major AI writing tool, coding assistant, and chatbot available today.

But most people using these tools — including many technical people — have a fuzzy understanding of what they actually are and how they work. That fuzziness leads to misplaced expectations: thinking AI can do things it can't, not knowing why it sometimes confidently produces nonsense, not understanding why prompting matters so much.

This guide explains large language models clearly. No maths, no code, no computer science degree required.

Table of Contents

What Is a Large Language Model?

A large language model is a type of artificial intelligence trained specifically to understand and generate human language.

The word "large" refers to the scale of the model — hundreds of billions of parameters (internal settings that shape how the model processes and generates text). The word "language" refers to what it works with. The word "model" is the computer science term for any system that represents something — in this case, a mathematical representation of how language works.

Put simply: an LLM is a very large, very complex mathematical system that has learned patterns from enormous amounts of text, and can use those patterns to predict and generate new text in response to your input.

It's not a database of answers. It's not connected to the internet by default. It doesn't "think" in the way humans do. It's a pattern-matching system — an extraordinarily sophisticated one — that generates text based on statistical probabilities learned from training.

That one sentence — it generates text based on statistical probabilities — explains most of what's surprising, impressive, or frustrating about AI tools. We'll come back to it throughout this guide.

Where Do LLMs Come From? The Training Process Explained

LLMs don't start knowing anything. They're trained from scratch on text data — and the training process is what gives them their capabilities.

Here's how it works in plain English.

Step 1: Gathering the training data

The first step is collecting an enormous amount of text. We're talking about hundreds of billions of words — books, websites, Wikipedia articles, academic papers, news articles, forum discussions, code repositories, and much more.

This corpus (the training dataset) represents a huge portion of the text available on the internet and in digitised books. For GPT-4, the training data is estimated to include well over a trillion words. The exact datasets used by different companies are largely proprietary and not fully disclosed.

Step 2: Predicting the next word

The actual training process involves a deceptively simple task: predicting the next word.

The model is shown a sequence of text — say, "The cat sat on the" — and asked to predict what word comes next. It makes a prediction. If the prediction is wrong, the model is adjusted slightly. If it's right, the model is reinforced. This happens billions of times, across the entire training dataset.

Through this process — predicting words, getting feedback, adjusting — the model learns the statistical patterns of language: which words tend to follow which other words, how sentences are structured, what a paragraph about climate change looks like compared to a paragraph about cooking, how a formal business email differs from a casual text message.

It sounds almost too simple to produce something as impressive as ChatGPT. But when you run this process at enough scale, across enough data, the results are remarkable.

Step 3: Computing at enormous scale

All of this requires massive computing infrastructure. Training a large model like GPT-4 requires thousands of specialised AI chips (GPUs or TPUs) running continuously for weeks or months. The computational cost of training a frontier LLM is estimated in the tens of millions of dollars. This is why only a handful of companies in the world — OpenAI, Google, Anthropic, Meta, Mistral — have the resources to train models at this scale.

How Does an LLM Generate Text?

When you type a prompt into an AI tool and see text appearing, here's what's actually happening.

The model processes your input

Your prompt is converted into numerical representations (tokens — more on these in a moment) and fed into the model. The model processes your entire input, including any system-level instructions from the application you're using (like "you are a helpful assistant who speaks casually").

The model predicts the next token

Based on your input and everything it learned during training, the model assigns probabilities to every possible next token. "The" might have a 12% probability. "A" might have an 8% probability. Some rare or completely irrelevant tokens might have 0.0001% probability.

The model then selects a token — usually favouring high-probability options, but with some controlled randomness to make the output feel natural rather than robotic. (This controlled randomness is why you can get slightly different answers to the same question.)

The model repeats, one token at a time

Once the first token is generated, it's added to the context. The model now predicts the next token based on your original input plus the token it just generated. This continues, one token at a time, until the model generates an end-of-sequence token or reaches a length limit.

This is why AI text sometimes starts strong and drifts — each token is predicted in the context of everything before it, but for very long outputs, the model is essentially extrapolating further and further from the original prompt.

Temperature: controlling the randomness

The "temperature" setting (which some AI tools expose to users) controls how random or deterministic the output is. Low temperature means the model more consistently picks the highest-probability tokens — more predictable, sometimes more robotic. High temperature means more randomness — more creative, sometimes less coherent. Most AI tools for writing use a temperature in the middle range to balance quality and variety.

What Makes an LLM "Large"?

The "large" in large language model refers specifically to the number of parameters.

Parameters are the internal numerical values that the model adjusts during training. Think of them as the knobs and dials that get tuned as the model learns patterns. A model with more parameters can represent more complex patterns — but also requires more data, more compute, and more memory to train and run.

To give you a sense of scale:

Beyond a certain size, something interesting happens: models start showing emergent capabilities — abilities that weren't explicitly trained and weren't present in smaller models. Common reasoning, multi-step problem solving, and understanding analogies appear to emerge at scale in ways that are not fully understood even by the researchers who build these systems.

This is one of the reasons the field moved so fast: each generation of scale produced new capabilities that surprised even the researchers.

What Are Tokens?

You'll hear the word "token" a lot when working with AI tools. Understanding what tokens are helps you understand how LLMs work and why they have certain limitations.

A token is not exactly a word. It's a chunk of text that the model processes as a unit. Depending on the tokenisation scheme, a token might be:

As a rough rule of thumb, one token is approximately 0.75 words in English. So 1,000 tokens is roughly 750 words, and 1,000 words is roughly 1,333 tokens.

Why does this matter?

Token limits. LLMs process both input and output in tokens, and there are limits to how many tokens a model can handle at once (this is the context window, explained next).

Pricing. AI API pricing is almost always in cost per token — input tokens and output tokens are typically priced separately.

Non-English languages. English is efficiently tokenised because the tokenisation schemes are optimised for it. Many other languages require more tokens to express the same ideas — meaning they're more expensive and slower to process.

What Is a Context Window?

The context window is the amount of text an LLM can "see" at once — the total of your input, the conversation history, any system instructions, and the output it generates.

Everything outside the context window doesn't exist for the model. It has no memory of conversations that happened before the current context window, no awareness of what it told you yesterday, and no ability to reference documents it isn't shown in the current session.

Context window sizes have grown dramatically:

A larger context window means the model can work with longer documents, maintain coherence across longer conversations, and process more reference material in a single session.

This is why you can paste an entire long PDF into Claude and ask questions about it — the document fits within the context window, so the model can reference it.

Why Do LLMs Hallucinate?

Hallucination is one of the most discussed and most important limitations of LLMs. It refers to the phenomenon where the model confidently produces incorrect, fabricated, or nonsensical information.

Examples include:

This happens for a fundamental reason: the model is not retrieving facts. It's generating text that is statistically likely to be correct, given the patterns it learned.

When you ask a question, the model doesn't look the answer up in a database. It generates what an answer to that question would look like — drawing on the patterns learned during training. Most of the time, those patterns correspond to true information. Sometimes they don't.

The model has no internal fact-checker. It doesn't know what it doesn't know. It can produce a confident, plausible-sounding sentence that is completely false — because confidence and plausibility are statistical properties, not truth markers.

Some hallucination is being reduced through techniques like retrieval-augmented generation (where the model can look up information in an external database) and improved training. But it has not been eliminated, and it's unlikely to be completely solved soon.

Practical implication: always verify AI-generated factual claims. For anything where accuracy matters — statistics, quotes, dates, citations, medical or legal information — treat AI output as a starting point for research, not a reliable source.

What Is Fine-Tuning?

Base LLMs trained on general text data are powerful but unrefined. They can complete text well, but they're not necessarily helpful, safe, or tuned for specific tasks.

Fine-tuning is the process of further training a base model on a smaller, more specific dataset to adjust its behaviour.

Instruction fine-tuning trains the model on examples of good instruction-following: here's a question, here's a good answer. After this, the model becomes much better at following user instructions rather than just completing text.

Task-specific fine-tuning trains the model on data from a specific domain — medical records, legal documents, customer support transcripts — to make it perform better in that domain.

Safety fine-tuning trains the model to avoid producing harmful content — by training on examples of what not to say and why.

Fine-tuning is why ChatGPT, Claude, and other consumer AI tools feel different from a raw base model — they've been fine-tuned to be helpful, to follow instructions, and to avoid problematic outputs.

What Is RLHF?

RLHF stands for Reinforcement Learning from Human Feedback. It's a training technique that has been central to making LLMs useful and somewhat safer.

Here's how it works:

  1. The model generates several responses to the same prompt.
  2. Human raters compare the responses and rank them — which is more helpful? Which is more accurate? Which is safer?
  3. A separate "reward model" is trained to predict how human raters would score any given response.
  4. The main LLM is then trained to maximise the reward model's score — in other words, to produce outputs that humans would rate highly.

This feedback loop is what makes AI assistants feel helpful rather than just plausible. It's why they tend to be polite, try to answer questions directly, acknowledge uncertainty, and avoid certain types of harmful content.

It's also why AI assistants sometimes have particular quirks — they're optimising for human ratings, which means they can sometimes be overly agreeable, verbose, or careful in ways that reflect the preferences of the people doing the rating.

What Are the Major LLMs in Use Today?

GPT-4 and GPT-4o (OpenAI)

The models behind ChatGPT and the OpenAI API. GPT-4 is the most widely deployed LLM in the world, powering thousands of applications beyond ChatGPT directly.

Claude 3 and Claude 4 family (Anthropic)

The models behind Claude.ai and the Anthropic API. Anthropic emphasises safety research and has published significant work on AI alignment. Claude models are generally considered strong for reasoning and long-form writing.

Gemini (Google DeepMind)

Google's LLM family, integrated into Google Search, Google Workspace, and available via API. Gemini is designed to be natively multimodal — processing text, images, audio, and video.

LLaMA (Meta)

Meta's open-weight model family. Unlike GPT and Claude, LLaMA models can be downloaded and run locally. This makes them important for privacy-sensitive applications, research, and developers who want to build without API dependence.

Mistral (Mistral AI)

A European AI company producing efficient, open-weight models. Mistral models are notable for being smaller but highly capable — important for applications where computing resources are limited.

What LLMs Can and Cannot Do

What they're good at

Understanding and generating text across domains. LLMs can write, summarise, translate, explain, and discuss topics across virtually every field — because they were trained on text from virtually every field.

Following complex instructions. Modern LLMs can follow detailed, multi-part instructions reliably. This is what makes them useful for structured tasks.

Reasoning through problems step by step. When prompted to think through problems carefully ("let's think step by step"), LLMs show significantly improved accuracy on reasoning tasks.

Adapting to context. Within a conversation, LLMs remember what's been discussed and adapt their responses accordingly.

Writing across many styles and formats. Formal reports, casual emails, technical documentation, creative fiction — LLMs can shift registers effectively when instructed.

What they struggle with

Factual accuracy, especially for specific details. Hallucination is a real and persistent problem.

Consistent mathematical and logical reasoning. LLMs can do basic arithmetic and some logical reasoning, but complex maths or multi-step logic is unreliable without additional tools.

Real-time or post-training information. Without web search access, LLMs only know what was in their training data — which has a cutoff date.

True understanding. LLMs don't understand language the way humans do. They produce text that looks like understanding because they've learned the patterns of understanding — but the underlying process is different.

Consistent persona across sessions. Without memory tools, an LLM starts fresh each conversation. It doesn't remember you.

How LLMs Are Changing Over Time

The field is moving fast. A few developments that are shaping where LLMs go next:

Multimodality. Modern LLMs increasingly handle images, audio, and video alongside text. This expands the range of tasks they can assist with.

Longer context windows. The amount of text an LLM can process at once continues to grow. This matters for tasks involving long documents, complex projects, or extended conversations.

Tool use and agentic behaviour. LLMs are increasingly able to use tools — search the web, run code, call APIs, interact with software — making them more useful for complex, multi-step tasks.

Retrieval-augmented generation (RAG). Connecting LLMs to external knowledge bases reduces hallucination and allows the model to provide up-to-date, verified information rather than relying solely on training data.

Smaller, more efficient models. Not every application needs a trillion-parameter model. There's significant research into smaller models that are fast, cheap to run, and nearly as capable as their larger counterparts for many tasks.

AI agents. Systems where multiple AI models work together on complex tasks, with each step informed by previous steps. This is an active area of development that's already producing practical applications.

Frequently Asked Questions

Is an LLM the same as AI? No. AI is the broad field. LLMs are one type of AI — specifically, AI systems trained to work with language. Other types of AI include computer vision systems, recommendation algorithms, robotics control systems, and more.

Does an LLM understand what it's saying? This is one of the most debated questions in AI research. The honest answer is that we don't know with certainty. LLMs produce text that looks like understanding — they can explain concepts, draw analogies, answer follow-up questions — but the underlying process is pattern matching rather than the kind of conceptual understanding humans have. Whether that constitutes "understanding" depends on how you define the word.

Can LLMs learn from our conversations? Not in real time. LLMs don't update their parameters based on individual conversations. What you tell them in a conversation shapes their responses within that conversation (through the context window), but doesn't permanently change the model. Retraining or fine-tuning happens separately, at significant computational cost.

Why do different AI tools give different answers to the same question? Several factors: different base models with different training data and parameters, different fine-tuning approaches, different system prompts (instructions given to the model by the application), different temperature settings, and the inherent randomness in how tokens are selected during generation.

Are LLMs dangerous? Like any powerful technology, LLMs have risks that need to be managed. These include producing harmful content, spreading misinformation through confident hallucination, enabling certain types of fraud, and potential economic disruption as they automate tasks. AI safety is a serious and active field of research. Whether the technology is net positive or negative depends largely on how it's developed and deployed.

How do I know if AI content is trustworthy? You can't tell from reading it alone — which is the core challenge. AI text can be indistinguishable from human text and still be wrong. The only reliable approach is to verify factual claims against primary sources, treat AI output as a draft rather than a final answer, and apply your own judgment and expertise. The more high-stakes the use of the content, the more rigorous your verification should be.

KA

Kehinde Adegbesan

Kehinde is the founder of Smart Tech Build and a passionate software developer. He writes about AI, web development, and tools that help businesses grow.

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