What Is a Large Language Model (LLM)? How It Works

Quick Answer

A large language model (LLM) is a type of AI system trained on massive amounts of text data to understand, generate, and manipulate human language. LLMs like GPT-4o (OpenAI), Claude (Anthropic), and Gemini (Google) work by predicting the most statistically likely next word based on patterns learned from billions of text examples. They power chatbots, writing assistants, code generators, and thousands of other AI applications.

LLM Definition in 3 Sentences

A large language model is an AI system built on deep learning that has been trained on enormous quantities of text from across the internet, books, code repositories, and other sources. During training, it learns statistical patterns in language so that it can predict what word or phrase comes next in any given context. Once trained, you can give it a prompt and it will generate relevant, coherent, and often surprisingly accurate responses based entirely on those learned patterns.

Famous Examples: GPT-4o, Claude, Gemini, and Llama 3

The LLMs most people encounter in 2026 come from a handful of major developers. OpenAI’s GPT-4o is the model behind ChatGPT. Anthropic’s Claude powers a range of business and consumer applications and is known for long context handling and careful, nuanced responses. Google’s Gemini 1.5 Pro integrates natively across Google’s product ecosystem and supports exceptionally long inputs. Meta’s Llama 3 is the leading open-source LLM, widely used by developers and researchers who want to run models on their own infrastructure without relying on a commercial API.

What Is a Large Language Model? Full Definition

A large language model is a neural network, specifically a transformer-based neural network, trained on a dataset of text so large that the resulting model develops a broad, flexible understanding of language. It can read a paragraph and answer questions about it, write a poem in a specified style, summarize a legal document, translate between languages, explain a piece of code, and draft a professional email, often switching between these tasks in a single conversation.

The ‘large’ in large language model refers to two things: the size of the training data, which is measured in hundreds of billions or even trillions of words, and the number of parameters in the model, which can range from a few billion to over a trillion. Parameters are the numerical weights the model adjusts during training to capture patterns in language. More parameters generally means more capacity to learn subtle and complex relationships, though the relationship between size and capability is not perfectly linear.

LLM vs AI: Are They the Same Thing?

No. AI is a broad field that includes rule-based systems, machine learning, robotics, computer vision, and much more. An LLM is one specific type of AI, a language-focused deep learning model. Every LLM is an AI system, but the vast majority of AI systems are not LLMs. When someone says their product is ‘powered by AI,’ they might mean an LLM, or they might mean a completely different type of system. The terms are not interchangeable, even though they are often used that way in casual conversation.

LLM vs Chatbot: Key Difference

A chatbot is an interface: a system designed to hold a text or voice conversation with a user. An LLM is an engine: the technology that processes language and generates responses. Most modern chatbots are powered by LLMs, but earlier chatbots used rigid rule-based logic with no language understanding at all. ChatGPT is a chatbot. The model that powers it, GPT-4o, is the LLM. When people say ‘I asked ChatGPT,’ they are usually describing the product. The LLM is what is doing the actual language work inside that product.

Why They Are Called ‘Large’

The word ‘large’ entered the vocabulary around 2018 and 2019 to distinguish this new generation of models from the smaller, task-specific models that came before. GPT-3, released by OpenAI in 2020, had 175 billion parameters and required hundreds of petaflops of compute to train. The models in common use in 2026 are even larger in terms of capability, though many newer architectures achieve better performance with fewer parameters through improved training techniques. ‘Large’ signals scale that was previously unimaginable in machine learning, not just bigger models, but a qualitatively different class of capability.

How Does an LLM Work? Simple Explanation

The ‘Autocomplete on Steroids’ Analogy

The simplest honest explanation of how an LLM works is this: it is autocomplete, but trained on everything humans have ever written, and running at a scale that produces outputs that feel genuinely intelligent.

When you type on your phone and it suggests the next word, that is a basic version of the same idea. Your phone’s autocomplete has seen what you type and learned your patterns. An LLM has seen billions of documents and learned the patterns of language across all of them. Ask it to finish the sentence ‘The capital of France is’ and it will say ‘Paris’ not because it looked it up, but because in its training data, that specific sequence of words was followed by ‘Paris’ with near certainty.

Ask it something more nuanced, like ‘Explain quantum entanglement to a ten-year-old,’ and it draws on patterns from physics textbooks, science education materials, popular science writing, and analogies used by teachers across thousands of documents. The result feels like understanding. Whether it constitutes genuine understanding in any deeper sense is one of the more interesting open questions in AI research.

What Is a Token? (With a Word-by-Word Example)

LLMs do not process text word by word. They process tokens, which are chunks of text that roughly correspond to words or parts of words. The exact boundaries depend on the tokenization method the model uses.

Here is a concrete example. The sentence ‘ChatGPT is surprisingly good at writing’ might be broken into the following tokens: ‘Chat’, ‘G’, ‘PT’, ‘ is’, ‘ surprisingly’, ‘ good’, ‘ at’, ‘ writing’. Some words become a single token. Uncommon words, long words, or technical terms are often split into multiple tokens. Numbers, punctuation, and whitespace are tokenized separately.

Why does this matter? Because LLMs are billed on token usage, because context windows are measured in tokens, and because understanding tokenization helps explain why LLMs sometimes struggle with tasks that require precise character-level manipulation, like counting letters in a word or reversing a string.

For most practical purposes, a rough rule of thumb is that one token is approximately three to four characters of English text, or about 0.75 words. A 1,000-word document is roughly 1,300 to 1,500 tokens.

How LLMs Predict the Next Word

When you send a message to an LLM, here is what happens at a high level. Your input is tokenized into a sequence of numbers. Those numbers pass through the model’s layers, where the attention mechanism evaluates how each token relates to every other token in the sequence. The final layer outputs a probability distribution across the entire vocabulary, assigning each possible next token a probability score. The model samples from this distribution to select the next token, then repeats the process, adding each new token to the context and predicting the one after it.

This happens thousands of times per second per response. What looks like a coherent paragraph being generated is actually a very fast sequence of next-token predictions, each one informed by everything that came before it in the current context.

What Is the Context Window?

The context window is the amount of text an LLM can process and hold in its working memory at one time. Everything outside the context window is invisible to the model. It cannot remember it, refer to it, or reason about it.

Context windows are measured in tokens. In 2026, the leading models have dramatically expanded their context windows compared to early LLMs. Claude supports context windows of up to 200,000 tokens, which is roughly equivalent to a full novel. GPT-4o supports up to 128,000 tokens. Google Gemini 1.5 Pro pushes even further, with a context window of up to 1 million tokens in its extended mode.

A larger context window means the model can read and reason across longer documents, maintain coherence in longer conversations, and handle tasks like summarizing a 300-page report in a single pass. For most everyday use cases, even a 16,000-token context is more than sufficient. For enterprise applications involving legal contracts, research documents, or lengthy codebases, the larger context windows become genuinely important.

The Transformer Architecture: No Maths Required

What Is a Transformer Model?

The transformer is the neural network architecture that makes modern LLMs possible. It was introduced in a landmark 2017 research paper titled ‘Attention Is All You Need,’ published by researchers at Google. Before transformers, the dominant architectures for language tasks were recurrent neural networks, which processed text sequentially, one word at a time. This made them slow to train and poor at capturing relationships between words that were far apart in a sentence.

Transformers solved this by processing all tokens in parallel and using a mechanism called attention to directly model relationships between any two tokens in the sequence, regardless of how far apart they are. This made training dramatically faster and the resulting models dramatically more capable.

What Is the Attention Mechanism?

Attention is the mechanism that allows a transformer to decide which parts of the input are most relevant when generating each part of the output. Think of it as a spotlight that the model can shine on different words in the input as it works through generating a response.

When an LLM reads the sentence ‘The trophy did not fit in the suitcase because it was too big,’ the attention mechanism allows it to link ‘it’ back to ‘trophy’ rather than ‘suitcase,’ because ‘trophy’ is the thing that is too big. This kind of reference resolution requires understanding relationships between words across a sentence, something earlier models struggled with. Attention handles it naturally because the model can directly compare any token to any other token and weight its representation accordingly.

In a large model, attention operates across many parallel ‘heads,’ each learning to focus on different types of relationships simultaneously. Some heads might specialize in grammatical agreement. Others might track entities across a paragraph. The model learns which types of relationships to pay attention to through training, not through explicit programming.

Why Transformers Changed Everything (2017)

Before 2017, natural language processing had been improving steadily but slowly. The transformer architecture changed the trajectory of the entire field. Within two years, OpenAI used it to build GPT-2. Within three, GPT-3 demonstrated capabilities that surprised even expert researchers. Within five, the technology was in the hands of hundreds of millions of users worldwide. The speed of progress from the 2017 paper to the products of today is one of the most rapid technology transitions in modern history, and the transformer architecture is the reason it happened.

How LLMs Are Trained

Training a large language model happens in three distinct stages. Each stage shapes the model’s behavior in a different way.

Stage 1: Pre-Training on Massive Text Datasets

In the first stage, the model is trained on a dataset that may contain trillions of words drawn from web pages, books, academic papers, code repositories, and other text sources. The training objective is simple: predict the next token. The model sees a sequence of tokens, tries to predict what comes next, gets feedback on how wrong it was, and adjusts its parameters to be less wrong next time. This process is repeated billions of times across the entire dataset.

By the end of pre-training, the model has developed rich internal representations of language, factual knowledge, reasoning patterns, and stylistic conventions, not because any of this was explicitly taught, but because it is all implicit in the statistical structure of the training data. Pre-training is the most computationally expensive stage and can cost tens of millions of dollars in compute for the largest models.

Stage 2: Fine-Tuning for Specific Tasks

A pre-trained model is capable but raw. It will predict text in the style of whatever it was trained on, which might include websites, forums, or other content not suited to a professional product. Fine-tuning takes the pre-trained model and continues training it on a smaller, curated dataset focused on the specific type of output the product needs. A customer service model might be fine-tuned on examples of helpful support conversations. A coding assistant might be fine-tuned on high-quality code paired with explanations.

Fine-tuning shifts the model’s distribution toward more useful, relevant outputs without losing the broad language understanding developed in pre-training. It is much cheaper than pre-training and can be done by smaller teams on more accessible hardware.

Stage 3: RLHF: Aligning with Human Preferences

The third stage, reinforcement learning from human feedback (RLHF), is what turns a capable language model into a helpful and reasonably safe assistant. Human raters evaluate model outputs and rank them from most to least preferred. A separate reward model is trained to predict those preferences. The main model is then fine-tuned using reinforcement learning to generate outputs the reward model scores highly.

RLHF is the primary technique responsible for the conversational, helpful character of models like ChatGPT and Claude. It teaches the model to give clear answers rather than evasive ones, to decline harmful requests, to ask for clarification when a question is ambiguous, and to match the tone and format that human users prefer. Without this stage, a highly capable language model can still produce outputs that are technically impressive but practically frustrating.

Examples of Large Language Models in 2026

OpenAI GPT-4o: Strengths and Best For

GPT-4o is OpenAI’s flagship model and the engine behind ChatGPT. It handles text, images, and audio inputs in a single model rather than routing between separate systems. Its strengths include strong general reasoning, reliable coding assistance, and a large ecosystem of integrations and third-party tools built around the ChatGPT platform. It is a solid default choice for general-purpose applications and benefits from the largest developer community of any commercial LLM.

Anthropic Claude: Strengths and Best For

Claude is Anthropic’s model family, with Claude Sonnet and Claude Opus serving different capability tiers. Claude is particularly strong in tasks requiring careful, nuanced reasoning, long document analysis, and outputs where safety and accuracy matter. Its 200,000-token context window is one of the largest commercially available, making it well-suited for enterprise document work, legal analysis, and complex research tasks. It tends to be more conservative and more precise than GPT-4o on tasks where getting the details right matters most.

Google Gemini 1.5 Pro: Strengths and Best For

Gemini 1.5 Pro is Google’s most capable model for general use and is natively integrated into Google Workspace, Search, and other Google products. Its standout feature is a context window of up to 1 million tokens in extended mode, which makes it exceptionally well-suited for applications involving very long documents or codebases. Teams already working in the Google Cloud ecosystem will find Gemini the most natural fit.

Meta Llama 3: The Open Source Leader

Llama 3 is Meta’s open-source LLM family, available for download and local deployment without a commercial API subscription. It has become the default choice for developers and organizations that want to run models on their own infrastructure, fine-tune on proprietary data without sending that data to a third-party API, or build products where ongoing API costs would be prohibitive. The trade-off is that running it requires significant compute resources and technical expertise to manage.

LLM Comparison Table: 2026

LLM	Developer	Context Window	Best For	Access
GPT-4o	OpenAI	128K tokens	General tasks, coding, multimodal	API / ChatGPT
Claude (Sonnet / Opus)	Anthropic	200K tokens	Long docs, nuanced reasoning, safety	API / Claude.ai
Gemini 1.5 Pro	Google	1M tokens (extended)	Very long inputs, Google ecosystem	API / Workspace
Llama 3	Meta	8K to 128K tokens	Open-source, local deployment	Download / HuggingFace
Mistral Large	Mistral AI	32K tokens	Efficient, European data sovereignty	API / Self-hosted
Command R+	Cohere	128K tokens	Enterprise RAG applications	API

What Can LLMs Do? Real Use Cases in 2026

The practical applications of large language models have expanded considerably since the early days of general-purpose chat. Here are the use cases seeing the most real-world deployment today.

Content creation and editing: Marketing teams use LLMs to draft blog posts, ad copy, email sequences, and social media content. Legal and compliance teams use them to review and edit documents for clarity and consistency. The model does not replace writers, but it dramatically accelerates the process of getting to a good first draft.

Code generation and review: Software developers use LLM-powered tools to write boilerplate code, explain unfamiliar codebases, catch bugs, translate between programming languages, and generate unit tests. GitHub Copilot, powered by an OpenAI model, is the most widely adopted example, but the category now includes dozens of specialized tools.

Document analysis and summarization: Lawyers, analysts, researchers, and consultants use LLMs to read long documents and extract key information. A model with a 200,000-token context window can process an entire contract or research paper and answer specific questions about it in seconds.

Customer support automation: LLM-powered support agents handle tier-one customer inquiries, answer product questions, and route complex issues to human agents. When built well, they resolve a significant share of support volume without human involvement and are available around the clock.

Data extraction and structuring: LLMs can read unstructured text, such as emails, PDFs, or web pages, and extract specific fields into structured formats. This is particularly valuable in industries like insurance, healthcare, and logistics where large volumes of information arrive in unstructured document form.

Search and knowledge retrieval: Combined with a technique called retrieval-augmented generation (RAG), LLMs can search a company’s internal knowledge base and generate accurate, source-grounded answers. This is the technology behind most enterprise AI assistants and internal chatbots.

LLM Limitations You Must Know

Understanding what LLMs cannot do is just as important as knowing what they can. Building products on top of LLMs without accounting for these limitations leads to systems that fail in predictable ways.

Hallucination: LLMs sometimes generate information that sounds plausible and is stated with confidence but is simply wrong. They may fabricate citations, invent statistics, or provide incorrect answers to factual questions. This happens because the model is predicting likely text, not retrieving verified facts. Any application where accuracy is critical needs a verification layer on top of the model output.

Training data cutoff: LLMs are trained on data up to a specific date. They have no knowledge of events that occurred after that cutoff unless you provide the information in the prompt. Models can be given access to web search or retrieval systems to partially address this, but the base model’s knowledge is frozen at training time.

Bias and inconsistency: Because LLMs are trained on human-generated text, they reflect the biases present in that text. They can produce outputs that are subtly or not-so-subtly biased along political, cultural, or demographic lines. They also produce inconsistent answers: ask the same question twice and you may get meaningfully different responses.

Cost at scale: Running LLM inference is not free. API costs are measured per token, and high-volume applications can accumulate significant monthly expenses. Teams building on commercial APIs should model their costs carefully before committing to an architecture, and consider whether a smaller, cheaper model can meet their accuracy requirements.

How to Build Applications on Top of LLMs

For most teams, building on top of LLMs means working with APIs rather than training models from scratch. The basic pattern is straightforward: your application sends a prompt to the LLM’s API along with any relevant context, the model returns a response, and your application uses that response in whatever way your product requires.

The more sophisticated patterns involve retrieval-augmented generation, where the application first searches a knowledge base to find relevant documents, then includes those documents in the prompt so the model can generate a grounded, accurate response. Agentic patterns take this further, giving the model access to tools like web search, code execution, or database queries, and allowing it to take sequences of actions to complete multi-step tasks.

The full step-by-step process for building an AI chatbot using LLMs is covered in our dedicated guide. If you are looking for a team to build on your behalf, our verified list of AI development companies covers the options across different budget ranges and specializations.

Final Thoughts

Large language models are genuinely new technology. The capabilities they unlock were not possible five years ago, and the rate of improvement has not slowed. At the same time, they are not magic, and the teams that use them most effectively are the ones that understand both what they can do and where they fall short.

The core idea is simpler than most people expect: predict the next word, at massive scale, with a powerful architecture. What emerges from that simple process is something that can read, write, reason, and generate in ways that continue to surprise even the people who build these systems.

If you want to go deeper on how these models are trained, the companion guide on AI model training covers the full technical process. For the broader context on how LLMs fit into the AI landscape, the article on generative AI is the natural next read.

What is the difference between an LLM and a chatbot?

A chatbot is a product: an interface that holds a conversation with a user. An LLM is the technology that powers it. Most modern chatbots are built on LLMs, but the terms are not interchangeable. ChatGPT is a chatbot. GPT-4o is the LLM inside it. Older chatbots were built on rule trees and keyword matching with no LLM involved at all.
How much does it cost to run an LLM?

Costs vary widely depending on the model and usage volume. Commercial APIs are typically priced per million tokens of input and output. For low-volume applications, monthly costs may be negligible. For high-volume products processing millions of requests, costs can run into thousands or tens of thousands of dollars per month. Teams with very high volume often explore fine-tuning smaller, cheaper models or running open-source models on their own infrastructure to reduce ongoing costs.
Can I run an LLM locally?

Yes, with caveats. Open-source models like Meta's Llama 3 can be downloaded and run on local hardware. Smaller versions, in the 7 billion or 13 billion parameter range, can run on a modern laptop with a GPU, though they are slower and less capable than the frontier models accessed through commercial APIs. Larger versions require server-grade hardware. Tools like Ollama and LM Studio make local deployment accessible for developers without deep infrastructure expertise.
What is the largest LLM in 2026?

The exact parameter counts of frontier models are not always publicly disclosed. Google's Gemini Ultra and OpenAI's GPT-4 architecture are believed to be among the largest deployed models, with estimates ranging from hundreds of billions to over a trillion parameters. The focus in 2026 has shifted somewhat from raw size to efficiency: newer models often match or exceed the capability of larger predecessors using fewer parameters through better training techniques.
Are LLMs and GPT the same thing?

No. GPT, which stands for Generative Pre-trained Transformer, is OpenAI's specific family of large language models. LLM is the general category. GPT-4o is an LLM. So is Claude, Gemini, and Llama. Saying 'LLM' is like saying 'car.' Saying 'GPT-4o' is like saying 'a specific Toyota model.' All GPT models are LLMs, but most LLMs are not GPT models.
What is RAG in the context of LLMs?

RAG stands for retrieval-augmented generation. It is a technique for grounding LLM outputs in specific, current, or proprietary information. Instead of relying solely on what the model learned during training, a RAG system first searches a knowledge base for relevant documents, then includes those documents in the prompt so the model can generate a response that is directly supported by the retrieved content. This dramatically reduces hallucination and allows LLMs to answer questions about information that postdates their training cutoff.
Do LLMs understand language or just predict it?

This is one of the genuinely interesting open questions in AI. Technically, LLMs predict tokens based on learned statistical patterns. They do not understand language in the way humans do, with grounding in physical experience, social context, and conscious awareness. At the same time, the internal representations they build during training capture semantic relationships, grammatical structures, and factual associations in ways that go well beyond simple pattern matching. Whether this constitutes a form of understanding is a question philosophers and researchers are actively debating. For practical purposes, what matters is that LLMs can perform language tasks at a level that frequently meets or exceeds human performance, regardless of how we label the underlying process.
What is the best LLM for business use in 2026?

There is no single answer because the best model depends on the use case. For general-purpose business tasks, drafting, summarizing, answering questions, and coding assistance, GPT-4o and Claude are both strong defaults with mature API ecosystems. For applications involving very long documents, Claude's 200,000-token context or Gemini's extended context give them an edge. For teams that need to keep data on their own infrastructure, Llama 3 is the leading open-source option. Most organizations run evaluations on their specific tasks before committing to a primary model.