What are Large Language Models (LLMs)?

TL;DR

• What it is: A large language model (LLM) is an AI system trained on huge amounts of text data that can understand and generate human language.
• How it works: It predicts the next word in a sequence, one token at a time, using a neural network architecture called a transformer.
• What it powers: Tools like ChatGPT, Claude, Gemini, Llama, and GitHub Copilot.
• Trained on: Trillions of words from books, Wikipedia, the open web, and code repositories.
• Size: Hundreds of billions to over a trillion internal parameters.
• Top examples in 2026: GPT-4o (OpenAI), Claude (Anthropic), Gemini (Google), Llama (Meta), Mistral, IBM Granite.
• Best at: Writing, coding, summarization, translation, customer support, and AI agents.
• Biggest risk: Hallucinations, which means confident but false output.
• Turning point: The transformer paper in 2017 and the launch of ChatGPT in November 2022.

What is a large language model?

A large language model (LLM) is an artificial intelligence system trained on massive amounts of text so that it can understand and generate human language. The word "large" refers both to the size of the training data (often trillions of words) and to the number of parameters inside the model (often hundreds of billions). LLMs are built on a neural network architecture called the transformer.

Five years ago, "large language model" was a phrase you'd hear at NLP conferences. Today you probably use one every week, maybe every day. ChatGPT drafts your emails. Claude reviews your code. Gemini summarizes your meetings. Llama runs quietly behind countless startup apps you've never thought twice about.

Under the hood, an LLM does something less mysterious than the marketing suggests. It predicts the next word. Then the next. Then the next. Run that fast over a long enough sequence and the cumulative effect looks like understanding. Whether it is understanding is a debate I'll leave to philosophers. The output is fluent. That much is undeniable.

Why LLMs matter

LLMs matter because they're the first software systems that can handle unstructured human language at scale.

Before LLMs, computers were good at structured tasks. Run a query. Match a keyword. Execute an instruction. They were bad at the messy, ambiguous, context-heavy way humans talk.

LLMs flipped that. You can type a vague request in plain English and get a useful answer back. You don't need to know SQL to pull a number out of a spreadsheet. You don't need to write a script to draft a memo. You describe what you want, and the model takes a swing.

The same model can summarize a research paper, translate Spanish to Mandarin, debug a Python function, and write a wedding toast. That kind of flexibility is rare in software. It's why nearly every industry is rushing to figure out where LLMs fit in their workflows, and why the term has stopped being academic and started being something my parents ask me about over dinner.

How LLMs work

LLMs work by converting text into numbers, processing those numbers through a transformer neural network, and predicting the most likely next token in a sequence.

To see how an LLM works, you need four ideas: deep learning, neural networks, transformers, and self-attention. They stack on top of each other.

Deep learning is the foundation

Deep learning is a type of machine learning that uses multi-layered neural networks to learn patterns from data. Instead of being programmed with explicit rules ("if the user types X, return Y"), LLMs learn statistical patterns from huge datasets. Each layer in the network pulls a slightly more abstract pattern out of the raw input. By the time you reach the top of the stack, the model has gone from "this is a sequence of characters" to "this is a question about cooking and the user wants a recipe."

Neural networks do the math

A neural network is a structure of connected nodes that pass information through layers, loosely inspired by neurons in a brain. Each node performs a small mathematical operation and passes the result to the next layer. With enough nodes, enough layers, and enough training data, neural networks can model patterns that no human could write down by hand.

Transformers are the breakthrough

A transformer is a neural network architecture that processes entire sequences of text in parallel, using a mechanism called self-attention. Vaswani and colleagues introduced it in a 2017 paper titled "Attention Is All You Need."

Before transformers, the main tools for handling language were recurrent neural networks (RNNs) and LSTMs, which processed words one at a time, in order. That was slow and made long-range context hard to capture.

Transformers read the whole sequence at once, in parallel. That changed two things. Training got much faster on GPUs, and the models got much better at holding context across long passages of text. Without the transformer, today's LLMs would not exist.

Tokenization and embeddings turn words into numbers

Tokenization is the process of breaking text into smaller units (tokens) so a model can process them. A token might be a whole word, part of a word, or a single character.

An embedding is a numerical vector that represents the meaning of a token in a high-dimensional space. Words used in similar ways end up with similar embeddings. "Dog" and "puppy" land near each other in vector space. "Puppy" and "spreadsheet" do not. The model also adds positional encoding so it knows that "dog bites man" is different from "man bites dog."

Self-attention is where the magic happens

Self-attention is a mechanism that lets a transformer weigh the importance of every token relative to every other token in a sequence. This is the piece that made everything click.

Mechanically, the model projects each embedding into three vectors: a query, a key, and a value. The query asks a question. The key advertises what each token has to offer. The value is the actual information passed along. By comparing queries against keys, the model decides which other tokens deserve focus when interpreting any given word. Stack a dozen or two dozen of these attention layers, and the model builds up a rich, context-aware understanding of the whole sequence.

When ChatGPT figures out that the "it" in your third sentence refers to a noun from your first sentence, self-attention is the reason.

How LLMs are trained

LLMs are trained in four main stages: pretraining, fine-tuning, reinforcement learning from human feedback (RLHF), and instruction tuning.

Training a frontier LLM is one of the most expensive things you can do in software. It's also where the model gets its personality, its blind spots, and most of its capabilities.

Step 1: Pretraining

Pretraining is the initial stage where an LLM learns language patterns by predicting the next token across trillions of words of text. The corpus typically includes books, news, Wikipedia, Reddit, GitHub repositories, scientific papers, and large slices of the open web. Data scientists clean it, deduplicate it, and filter out the worst content.

Then the model trains itself, more or less. This is called self-supervised learning. There's no human sitting there labeling every example. Instead, the model plays a guessing game: cover up a word, try to predict it, check the answer, adjust. Run that loop billions of times across trillions of tokens, and the model gradually internalizes grammar, facts, writing styles, and reasoning patterns.

Under the hood, adjustments happen through backpropagation and gradient descent. A loss function scores how wrong the prediction was, and the model nudges its weights to do better next time. Repeat. Forever, if your GPU bill allows.

Step 2: Fine-tuning

Fine-tuning is the process of adapting a pretrained model to a specific task or domain using a smaller, focused dataset. A medical company might fine-tune on clinical notes. A law firm might fine-tune on case law. The base model stays the same; the new dataset shifts its behavior toward the target use case.

Step 3: Reinforcement learning from human feedback (RLHF)

RLHF is a training method where human reviewers rank model outputs, and the model learns to prefer the responses humans rate higher. Pretrained models will happily say strange, biased, or unhelpful things. RLHF is the cleanup step. That's where most of the "helpful and polite" behavior in modern chatbots comes from.

Step 4: Instruction tuning

Instruction tuning is a fine-tuning method that trains a model to follow user instructions rather than continue text. A pretrained model doesn't know that prompts are commands. Left alone, it might extend your text rather than answer your question. Instruction tuning teaches it to treat user input as a request and respond accordingly. Most LLMs you interact with have been through this.

Three ways to use a trained LLM

Popular LLMs in 2026

The leading LLMs in 2026 include GPT-4o, Claude, Gemini, Llama, Mistral, and IBM Granite. Here's how they compare:

The space moves fast enough that any list you read is partly out of date by the time you finish reading it.

What people use LLMs for

LLMs are used for content creation, customer support, code generation, summarization, translation, sentiment analysis, knowledge retrieval, tutoring, and AI agents. A short tour:

Writing. Drafting emails, blog posts, product descriptions, marketing copy, and yes, ad copy you'll see in your inbox tomorrow. The output isn't always good, but the first draft is fast.

Customer support. Chatbots that can answer questions instead of looping you through a menu. Many companies now run their tier-1 support through an LLM, with humans handling the trickier escalations.

Code generation. GitHub Copilot, Cursor, and Amazon CodeWhisperer help developers write, refactor, debug, and translate code across languages. For boilerplate, it's a huge time saver. For architecture decisions, you still need a human.

Summarization. Long reports, meeting transcripts, research papers, legal documents. An LLM can compress them into a tight summary in seconds. Whether the summary is accurate is a separate question.

Translation. Modern LLMs handle 100+ languages with fluency that often matches dedicated translation systems and sometimes beats them.

Sentiment analysis. Reading thousands of customer reviews or social media posts and pulling out the mood. Useful for product teams who want to know what users think.

Knowledge retrieval (RAG). Pair an LLM with a search system over your company's documents and you get a Q&A bot that can answer questions about your internal wiki without making up answers (well, mostly). This is called retrieval-augmented generation.

Tutoring and learning. A patient explainer that can answer the same question seventeen different ways. Imperfect, but better than no help at all.

AI agents. Plug an LLM into memory, tools, and APIs, and it stops being a chatbot and starts being a system that can take action. Book a flight. File a ticket. Read your inbox and reply to the easy stuff. This is the frontier right now, and the area where most of the next wave of products will land.

What LLMs do well

LLMs are best at handling open-ended language tasks where flexibility matters more than precision. A few specific things they're good at:

• One model handles many tasks. You don't need a different system for summarization, translation, and Q&A.
• The interface is plain language. No training required for the user.
• They're fast. A draft email in two seconds beats a draft email in twenty minutes.
• Few-shot learning works. Show the model two examples of the format you want and it usually nails the third.
• Fine-tuning lets you specialize. A general model can become a domain expert without starting from scratch.

Where LLMs fall short

The main limitations of LLMs are hallucinations, bias, high cost, energy use, privacy risks, and a lack of real-world grounding. This is the part most marketing decks skip.

Hallucinations. LLMs make things up. With total confidence. The model isn't lying; it's predicting plausible text, and plausible isn't the same as true. Citations get invented. Quotes get fabricated. Numbers drift. If accuracy matters, you have to verify.

Bias. The training data is the internet, and the internet has opinions. Models absorb those opinions, including the ugly ones. Mitigation helps. It doesn't eliminate.

Cost. Training a frontier model can run into the tens of millions of dollars in compute alone. Running inference at scale also adds up. The economics are improving but they're not free.

Energy. All that compute uses a lot of electricity. The environmental story is real, even if the exact numbers are debated.

Privacy. If you paste confidential data into a public LLM, that data may end up in training runs, logs, or worse. Most enterprises now have policies about what you can and can't share.

Prompt injection. Adversaries can craft inputs that trick a model into ignoring instructions or leaking information. It's a young field and the attacks are getting more creative faster than the defenses are catching up.

Knowledge cutoff. Without a retrieval system, an LLM only knows what was in its training data. Ask it about yesterday's news and you'll get a guess or a refusal.

No real understanding. LLMs model patterns in text. They don't have lived experience, common sense in the way humans do, or a grounded sense of cause and effect. They can be brilliant on one question and absurdly wrong on the next.

Used carefully, LLMs are powerful. Used carelessly, they're a confident misinformation engine. Both things are true.

A short history of LLMs

The LLM era began in 2017 with the publication of the transformer paper, and accelerated rapidly after the launch of ChatGPT in November 2022.

Since then the pace has been hard to keep up with. New labs, new models, new architectures, new product categories. Mamba and diffusion-based language models are starting to challenge the transformer's dominance. Small language models are getting good enough to run on phones.

Where this is going

The next wave of LLM development focuses on multimodality, longer context, smaller on-device models, and agentic AI. A few trends worth watching:

• Multimodal models. Text plus images plus audio plus video in one model. Already here in early form. Going to get much better.
• Longer context. Million-token windows are the new normal at the frontier. Soon entire books, codebases, or conversation histories fit in a single prompt.
• Smaller models on edge devices. Phones, laptops, cars. Privacy gets easier when the model runs locally.
• AI agents. Less chat, more action. The shift from "tell me about X" to "go do X" is happening now.
• Better alignment. As models get more capable, the safety work matters more. Expect more research, more red-teaming, more regulation.

Glossary of LLM terms

Backpropagation: A training algorithm that calculates how each parameter in a neural network contributed to an error, so the network can be adjusted.

Context window: The maximum amount of text (measured in tokens) a model can consider at one time when generating a response.

Embedding: A numerical vector that represents the meaning of a token, word, or phrase in a high-dimensional space.

Fine-tuning: Continuing to train a pretrained model on a smaller, task-specific dataset to specialize its behavior.

Hallucination: When an LLM generates output that sounds plausible but is factually false.

Inference: The process of running a trained model to generate predictions or outputs from new input.

Parameter: A learned numerical value inside the model that influences how it processes input and generates output.

Pretraining: The initial training phase where a model learns general language patterns from a massive corpus of text.

Prompt: The text input you give an LLM to elicit a response.

RAG (retrieval-augmented generation): A technique that combines an LLM with an external knowledge source so the model can ground its answers in real documents.

RLHF (reinforcement learning from human feedback): A training method that uses human preferences to steer model output toward helpful and safe responses.

Self-attention: A mechanism inside transformers that lets each token weigh the importance of every other token in a sequence.

Token: A unit of text (a word, sub-word, or character) that an LLM processes.

Transformer: The neural network architecture behind modern LLMs, introduced in 2017.

Zero-shot / few-shot learning: Performing a new task with no training examples (zero-shot) or just a few in the prompt (few-shot).

Frequently Asked Questions (FAQs)

Rohit Mishra

GEO Strategist at Writesonic

Rohit is an GEO Strategist at Writesonic with nearly a decade of experience driving organic growth across industries. Over the past 9 years, he has partnered with brands across BFSI, ecommerce, and B2B SaaS, helping them turn search visibility into measurable revenue. His expertise lies in Generative Engine Optimization (GEO) and AI Search, where he crafts strategies that help brands earn placement in answers from ChatGPT, Perplexity, Google AI Overviews, and beyond.