What is a Large Language Model (LLM)?

What is an LLM?

A Large Language Model (LLM) is a type of artificial intelligence model designed to understand, generate, and work with human language. The term "large" refers to two things: the massive amount of text data used to train these models, and the enormous number of parameters (billions or even trillions) they contain.

At its core, an LLM is a next-token prediction engine. Given a sequence of text, it predicts what token (word, part of a word, or symbol) is most likely to come next. This simple mechanism, when scaled to billions of parameters and trained on trillions of tokens, produces remarkably capable language understanding and generation.

LLMs power the AI tools you use every day: ChatGPT, Claude, Gemini, Copilot, and many more. They can write code, answer questions, translate languages, summarize documents, and much more.

How LLMs Work

LLMs work through a process called autoregressive generation — they generate text one token at a time, using all previous tokens as context.

The Basic Process

1. Input Processing: Your text is broken into tokens (subwords, words, or characters)
2. Embedding: Each token is converted into a numerical vector (a list of numbers)
3. Attention: The model calculates how each token relates to every other token
4. Prediction: Based on all the context, the model predicts the next token
5. Generation: The predicted token is added to the input, and the process repeats

This process continues token by token until the model generates a stop signal or reaches a length limit.

Why "Large"?

The "large" in LLM refers to scale:

• Parameters: Modern LLMs have billions of parameters (GPT-4 reportedly has over 1 trillion)
• Training data: Models are trained on hundreds of billions to trillions of tokens from books, websites, code, and more
• Compute: Training requires thousands of GPUs running for weeks or months

The relationship between scale and capability follows "scaling laws" — generally, more parameters and more data produce more capable models.

The Transformer Architecture

Nearly all modern LLMs are based on the Transformer architecture, introduced in the 2017 paper "Attention Is All You Need" by Google researchers.

Key components of the Transformer:

• Self-Attention: Allows the model to weigh the importance of each token relative to every other token in the sequence
• Multi-Head Attention: Multiple attention mechanisms running in parallel, each learning different relationships
• Feed-Forward Networks: Process the attention outputs through dense neural network layers
• Positional Encoding: Since Transformers process all tokens simultaneously (not sequentially), positional information must be explicitly added
• Layer Normalization: Stabilizes training by normalizing activations

Modern LLMs typically use only the decoder part of the original Transformer (called "decoder-only" or "autoregressive" Transformers). This is because they generate text left-to-right, one token at a time.

Read our full Transformer architecture guide →

How LLMs Are Trained

Training an LLM happens in multiple stages:

1. Pre-training

The model learns language patterns by predicting the next token on massive text datasets. This is the most expensive phase, requiring thousands of GPUs and weeks of computation. The model learns grammar, facts, reasoning patterns, and world knowledge.

2. Supervised Fine-tuning (SFT)

The pre-trained model is further trained on high-quality instruction-response pairs. This teaches the model to follow instructions and have conversations, rather than just predicting text.

3. Alignment (RLHF/DPO)

The model is aligned with human preferences using techniques like RLHF (Reinforcement Learning from Human Feedback) or DPO (Direct Preference Optimization). This makes the model helpful, harmless, and honest.

Cost: Training a frontier LLM from scratch costs $100M+. Fine-tuning existing models is much cheaper ($100-$10,000).

Types of LLMs

Popular LLMs in 2026

• GPT-4 / GPT-4o (OpenAI) — Leading proprietary model, excellent at coding and reasoning
• Claude 3.5 / Claude 4 (Anthropic) — Strong at analysis, writing, and safety
• Gemini Pro / Ultra (Google) — Integrated with Google ecosystem, strong multimodal
• Llama 3 (Meta) — Best open-source model, widely used for self-hosting
• Mistral / Mixtral (Mistral AI) — Efficient open-source models with MoE architecture
• Qwen 2.5 (Alibaba) — Leading Chinese open-source model

Read our full LLM comparison guide →

Real-World Applications

LLMs are used across industries:

• Chatbots & Assistants — Customer support, personal assistants (ChatGPT, Claude)
• Code Generation — Writing, reviewing, and debugging code (GitHub Copilot, Cursor)
• Content Creation — Writing articles, marketing copy, creative content
• RAG Systems — Retrieval-Augmented Generation for knowledge-grounded answers
• Translation — High-quality machine translation across 100+ languages
• Analysis — Summarizing documents, extracting insights, data analysis
• Education — Tutoring, explaining concepts, generating practice problems

Limitations and Challenges

Despite their capabilities, LLMs have important limitations:

• Hallucinations — LLMs can generate confident but incorrect information
• Knowledge Cutoff — Models only know information from their training data
• Context Window — Limited amount of text they can process at once (though this is increasing)
• Reasoning Gaps — Struggle with complex multi-step reasoning and mathematics
• Bias — Can reflect biases present in training data
• Cost — Running large models is expensive (GPU compute)

Learn more about LLM hallucinations →