How Large Language Models Actually Work: A Plain English Explanation

Everyone Uses Them, Almost Nobody Understands Them

There is something funny about the current state of technology: hundreds of millions of people use large language models every day — chatting with ChatGPT, asking Claude to debug their code, using Gemini to summarise emails — yet almost nobody understands how these systems actually work. And that is not a criticism. The companies building these models are not exactly going out of their way to explain the internals in plain language.

Most explanations I have read fall into two categories. The first is the oversimplified version: "It predicts the next word." True, but so incomplete that it is almost misleading. The second is the academic version: dense papers full of matrix multiplication notation and gradient descent equations that make your eyes glaze over if you do not have a machine learning background.

I want to try something different here. I am going to explain how large language models work in a way that a curious, intelligent person without a CS degree can follow. There will be some simplifications — this is not a textbook — but nothing that is flat-out wrong. By the end, you should have a solid mental model of what happens when you type a question into ChatGPT and press Enter.

The Transformer Architecture: Where It All Starts

Every major LLM in 2026 — GPT-4o, Claude 3.5, Gemini 2.0, Llama 3, Mistral — is built on an architecture called the Transformer. It was introduced in a 2017 paper by researchers at Google with the iconic title "Attention Is All You Need." That paper is, without exaggeration, one of the most important publications in the history of computer science.

Before transformers, language models used architectures called RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory networks). These processed text sequentially — one word at a time, from left to right. This was slow and made it hard for the model to understand long-range dependencies. If a document mentioned a character's name on page one, an RNN would struggle to remember it by page ten.

Transformers solved this with a mechanism called self-attention, which allows the model to look at all parts of the input simultaneously and figure out which words are most relevant to each other. More on this in a moment.

The Basic Structure

A transformer model has two main components:

1. Encoder — Reads and understands the input text
2. Decoder — Generates the output text

The original 2017 paper used both an encoder and a decoder (hence "Attention Is All You Need" was about machine translation). Modern LLMs like GPT-4 use only the decoder part. They generate text by predicting one token at a time, conditioning each prediction on everything that came before it.

The model is made up of layers — typically dozens to over a hundred. Each layer consists of:

• A self-attention mechanism (the key innovation)
• A feed-forward neural network (processes each position independently)
• Layer normalization and residual connections (technical details that help with training stability)

Tokenization: How Text Becomes Numbers

Neural networks cannot process text directly. They work with numbers. So before any text enters the model, it gets converted into tokens — numerical representations of text chunks.

Tokenization does not simply split text into individual words. Instead, it uses algorithms like BPE (Byte Pair Encoding) to break text into subword units. Common words get their own tokens, while rare words are split into smaller pieces.

For example, the word "understanding" might be a single token, while "unfathomable" might be split into "un", "fath", "om", "able." Numbers, code, and non-English text often get split into smaller chunks as well.

Why this matters:

• The model has a fixed vocabulary of tokens (typically 50,000-100,000). Everything it reads or writes is composed from this vocabulary.
• The number of tokens in a prompt determines how much of the model's context window it uses. GPT-4o has a 128K token context window; Claude 3.5 offers 200K. These limits determine how much text the model can "see" at once.
• Token count affects cost — API pricing is usually per token.

You can experiment with tokenization yourself. OpenAI has a free tokenizer tool at platform.openai.com/tokenizer that shows how any text gets split into tokens.

# Example: Simple tokenization concept (not actual LLM tokenizer)
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("gpt2")
text = "Large language models are fascinating"
tokens = tokenizer.encode(text)
decoded = [tokenizer.decode([t]) for t in tokens]

print(f"Token IDs: {tokens}")
print(f"Decoded tokens: {decoded}")
# Token IDs: [16962, 3303, 4981, 389, 21426]
# Decoded tokens: ['Large', ' language', ' models', ' are', ' fascinating']

The Attention Mechanism: The Magic Ingredient

Self-attention is what makes transformers so powerful. The intuition behind it is simple: when processing a word, the model should pay more attention to some other words than others.

Consider this sentence: "The cat sat on the mat because it was tired."

What does "it" refer to? The cat, obviously. But how does a computer figure that out? Self-attention allows the model to compute a relevance score between "it" and every other word in the sentence. The score between "it" and "cat" will be high, while the score between "it" and "mat" will be low. The model uses these scores to create a representation of "it" that is heavily influenced by the meaning of "cat."

Multi-Head Attention

The real transformer does not compute attention just once — it computes it multiple times in parallel using different "heads." Each head can learn to attend to different types of relationships:

• One head might focus on syntactic relationships (subject-verb agreement)
• Another might focus on semantic relationships (pronouns and their antecedents)
• Another might focus on positional proximity (nearby words)
• Yet another might focus on high-level thematic connections

The outputs of all heads are combined to create a rich, multi-faceted representation of each token.

The Simplified Math

Without getting into matrix notation, here is what happens in self-attention:

1. For each token, the model creates three vectors: Query (Q), Key (K), and Value (V)
2. The Query of one token is compared against the Keys of all other tokens to produce attention scores
3. These scores are normalized (using softmax) so they sum to 1
4. The scores are used to take a weighted average of the Value vectors

Think of it like a library search. The Query is your search query. The Keys are the index entries of every book. The Values are the actual content of each book. You compare your query against every index, figure out which books are most relevant, then combine the content of the relevant books weighted by their relevance scores.

The Training Process: Learning from the Internet

Training an LLM is conceptually straightforward but computationally enormous.

Step 1: Pre-training

The model is trained on a massive corpus of text — web pages, books, academic papers, code repositories, forum discussions, and more. For models like GPT-4 and Claude, this corpus contains trillions of tokens.

The training objective is simple: predict the next token. Given a sequence of tokens, the model tries to predict what comes next. If the actual next token is "dog" and the model predicted "cat," the model's parameters are adjusted slightly to make "dog" more likely next time it sees similar context.

This process repeats billions of times across the entire training corpus. The model is not memorising text — it is learning statistical patterns about how language works, what facts are commonly stated, how reasoning typically flows, and what code syntax looks like.

The scale of pre-training is staggering:

Step 2: Fine-Tuning

After pre-training, the raw model is a powerful text predictor but a terrible assistant. It will happily continue any prompt in any direction — generating toxic content, making up facts, or ignoring the user's actual question. It does not know that it is supposed to be helpful.

Fine-tuning adjusts the model's behaviour using curated examples of desirable conversations. Human trainers create thousands of prompt-response pairs that demonstrate the kind of helpful, honest, and harmless behaviour the company wants.

Step 3: RLHF (Reinforcement Learning from Human Feedback)

RLHF is the secret sauce that makes modern chatbots feel conversational and aligned with human preferences. The process works like this:

1. The model generates multiple responses to the same prompt
2. Human raters rank these responses from best to worst
3. A separate "reward model" is trained to predict human preferences
4. The LLM is further trained using reinforcement learning to maximise the reward model's score

This is why ChatGPT and Claude feel different despite using similar underlying architectures — their RLHF training reflects different priorities and values. Anthropic, the company behind Claude, emphasises safety and nuance. OpenAI has optimised for broad capability and engagement.

Some models now use variations like DPO (Direct Preference Optimization) or RLAIF (RL from AI Feedback), which are more efficient than classic RLHF but achieve similar outcomes.

Temperature and Top-p: Controlling Randomness

When generating text, the model produces a probability distribution over its entire vocabulary for each next token. How the model selects from this distribution is controlled by two key parameters.

Temperature

Temperature controls how "random" the output is. Imagine the model predicts that the next token probabilities are:

• "the": 40%
• "a": 25%
• "one": 15%
• "some": 10%
• Everything else: 10%

At temperature 0, the model always picks the highest-probability token ("the"). The output is deterministic and repetitive.

At temperature 1, the model samples according to the true probabilities. There is a 40% chance it picks "the," 25% for "a," and so on. The output is creative and varied.

At temperature > 1, the distribution flattens further, making unlikely tokens more probable. The output becomes increasingly chaotic and nonsensical.

Top-p (Nucleus Sampling)

Top-p is a smarter way to control randomness. Instead of adjusting the entire distribution, it limits the selection to the smallest set of tokens whose cumulative probability exceeds a threshold p.

With top-p = 0.9, the model considers only the most likely tokens that together account for 90% of the probability mass. This eliminates extremely unlikely tokens while still allowing creative variation.

Most chatbot deployments use temperature around 0.7-1.0 with top-p around 0.9-0.95. For code generation, lower temperature (0.2-0.5) produces more reliable output. For creative writing, higher temperature (0.8-1.2) produces more interesting text.

Context Windows: The Memory Limit

A context window is the maximum number of tokens a model can process in a single conversation turn. This includes both your input and the model's output.

A larger context window means the model can process longer documents, maintain longer conversations, and consider more information when generating responses. Gemini's 2M token window is large enough to process entire novels or codebases.

However, there is a nuance: models do not attend equally well to all parts of their context window. Research has shown that most models are better at using information at the beginning and end of the context, with a tendency to lose track of details in the middle. This is sometimes called the "lost in the middle" problem.

Hallucinations: When Models Confidently Make Things Up

This is probably the most important limitation to understand. LLMs hallucinate — they generate text that sounds plausible and confident but is factually wrong. They will cite papers that do not exist, attribute quotes to people who never said them, and describe events that never happened.

Why does this happen? Because the model is fundamentally a statistical pattern matcher, not a knowledge database. It generates text that is likely given the patterns it learned during training. If a plausible-sounding but incorrect fact fits the statistical pattern, the model has no mechanism to distinguish it from a real fact.

Examples of hallucination:

• Citing a research paper with a real-sounding title, plausible author names, and a specific DOI — none of which exist
• Claiming a historical event happened on a specific date that is off by several years
• Generating code that uses an API function that does not exist in the library
• Providing legal advice citing statutes that were never enacted

How to mitigate hallucinations:

• Always verify factual claims from LLM output against authoritative sources
• Use RAG (Retrieval-Augmented Generation) — a technique where the model first retrieves relevant documents from a database, then generates answers based on those documents
• Ask the model to cite sources and then check those sources manually
• For code, always test the output — do not trust it blindly
• Use lower temperature settings for factual tasks

How LLMs Differ from Search Engines

People often conflate LLMs with search engines, but they are fundamentally different tools.

The most effective approach is to use both together. Use an LLM for reasoning, synthesis, and content generation. Use a search engine for fact-checking, finding recent information, and accessing primary sources.

Popular Models Compared

The LLM landscape in early 2026 looks like this:

GPT-4o (OpenAI)

The most widely used LLM. Excellent at general-purpose tasks, coding, and multimodal understanding (text + images + audio). The "o" stands for "omni" — it natively handles multiple input and output modalities. Available through ChatGPT and the OpenAI API.

Claude 3.5 Sonnet (Anthropic)

Known for strong reasoning, long context handling (200K tokens), careful and nuanced responses, and excellent coding ability. Claude tends to be more cautious and less likely to generate harmful content. The free tier on claude.ai is generous.

Gemini 2.0 (Google)

Google's flagship model with an enormous 2M token context window. Deeply integrated with Google Workspace (Docs, Gmail, Drive). Strong multimodal capabilities. The Pro version is available for free through the Gemini app.

Llama 3 (Meta)

The most capable open-source model family. The 405B parameter version rivals GPT-4 in many benchmarks. Being open-source, it can be run locally on your own hardware, fine-tuned for specific use cases, and deployed without API costs. The smaller 8B and 70B variants run on consumer hardware.

Running Models Locally

One of the most exciting developments in 2026 is the ability to run powerful LLMs on personal hardware. Tools like Ollama, LM Studio, and llama.cpp make it straightforward to download and run open-source models on your laptop or desktop.

# Running Llama 3 8B locally with Ollama
ollama pull llama3:8b
ollama run llama3:8b "Explain quantum computing in simple terms"

A laptop with 16GB of RAM can comfortably run 7-8B parameter models. A desktop with 32GB of RAM and a decent GPU can handle 13-30B parameter models. For the full 70B or 405B models, you need serious hardware — multiple high-end GPUs with large VRAM.

Local models are slower than cloud APIs, and smaller models are less capable than frontier models like GPT-4o or Claude 3.5. But they offer complete privacy (your data never leaves your machine), zero API costs, and the ability to fine-tune for specific tasks.

What LLMs Genuinely Cannot Do

The hype around LLMs sometimes obscures their real limitations. Here is an honest assessment:

They cannot reason reliably about novel problems. LLMs pattern-match against training data. When a problem is genuinely novel — unlike anything in the training set — the model's "reasoning" becomes unreliable. It may produce something that looks like reasoning but is actually pattern-matching against superficially similar problems.

They cannot do math consistently. Large number arithmetic, multi-step calculations, and mathematical proofs are unreliable without external tools. This is why models increasingly use code execution (Python) for math tasks rather than trying to compute in their heads.

They do not have persistent memory. Each conversation is independent (unless the platform specifically implements memory features). The model does not remember your conversation from yesterday unless that history is explicitly included in the current context window.

They cannot access the internet in real-time (in their base form). The model's knowledge is frozen at its training cutoff date. Some platforms add real-time search capabilities as a plugin, but the base model itself does not browse the web.

They do not understand in the way humans do. This is a philosophical point, but an important one. LLMs process statistical patterns in text. Whether this constitutes "understanding" is a deep debate in AI research. What is clear is that their "understanding" is fundamentally different from human understanding — they lack embodied experience, common sense grounding, and causal reasoning in many situations.

Where This Is All Heading

The field is moving at a pace that makes predictions risky, but some trends seem clear:

• Multimodality is becoming standard. Models will natively process text, images, audio, video, and structured data. The distinction between a "language model" and a "vision model" is disappearing.
• Reasoning capabilities are improving. Techniques like chain-of-thought prompting and reasoning-focused training are making models better at multi-step problem solving.
• Models are getting smaller and more efficient. Distillation and quantization techniques mean that models running on a phone in 2027 may match the capability of cloud models from 2024.
• Tool use is becoming central. Rather than trying to do everything with pure language generation, models are learning to use external tools — calculators, code interpreters, web browsers, databases — and combining them with their language abilities.
• Safety and alignment remain open problems. As models become more capable, ensuring they behave as intended becomes more critical and more difficult.

Understanding how LLMs work is not just academic curiosity. It is practical knowledge that helps you use these tools more effectively, spot their limitations, and make informed decisions about when to trust their output and when to verify independently. The better you understand the machine, the better you can collaborate with it.