What Are Large Language Models? The Complete Guide

Large language models have gone from a niche research topic to the most talked-about technology in the world. ChatGPT, Claude, Gemini, and their counterparts have demonstrated capabilities that seemed like science fiction just a few years ago: writing essays, explaining complex topics, generating code, analyzing documents, and holding nuanced conversations. But what exactly are large language models, and how do they work?

This guide provides a comprehensive overview of LLMs: what they are, how they are built, what they can and cannot do, and why they matter.

Defining Large Language Models

A large language model (LLM) is a neural network trained on massive amounts of text data that can understand and generate human language. The "large" in the name refers to both the size of the training data (often trillions of words) and the number of parameters in the model (billions to trillions of learnable weights).

At their core, LLMs are built on the transformer architecture, specifically the decoder-only variant. They are trained with a deceptively simple objective: predict the next token in a sequence. Given the text "The capital of France is," the model learns to assign high probability to "Paris." Despite this simple training objective, LLMs develop remarkably sophisticated capabilities.

LLMs are, at their most fundamental level, next-token prediction machines. The surprising depth of their abilities emerges from learning this simple task across an enormous breadth of human knowledge.

How LLMs Work Under the Hood

Tokenization

Before an LLM can process text, it must be converted into numbers. Tokenization splits text into subword units called tokens. Common words might be single tokens, while rare words are split into pieces. The sentence "unbelievable" might become ["un", "believ", "able"]. Most modern LLMs use between 30,000 and 100,000 unique tokens in their vocabulary.

The Transformer Architecture

Each token is converted into an embedding vector, and positional information is added. The sequence of embeddings then passes through dozens or hundreds of transformer layers, each consisting of:

• Self-attention: Each token computes attention weights with every preceding token, building contextual understanding
• Feed-forward network: A two-layer MLP that transforms each token's representation independently
• Layer normalization and residual connections: Stabilize training and enable information flow through deep networks

After all layers, the final representation is projected to the vocabulary size, and a softmax function produces a probability distribution over possible next tokens.

Autoregressive Generation

LLMs generate text one token at a time. After predicting a token, it is appended to the input, and the process repeats. Temperature and sampling strategies (top-k, top-p) control the balance between deterministic and creative output.

The Training Pipeline

Building an LLM involves multiple training stages:

Pre-training

The model is trained on a massive corpus of text from the internet, books, code, and other sources. This stage teaches the model language, facts, reasoning patterns, and general knowledge. Pre-training requires enormous compute: GPT-4-class models reportedly cost over $100 million to train.

Supervised Fine-Tuning (SFT)

The pre-trained model is fine-tuned on curated examples of high-quality conversations and task completions. Human annotators write ideal responses to a variety of prompts, teaching the model the desired format and behavior.

Reinforcement Learning from Human Feedback (RLHF)

Human raters compare pairs of model outputs and indicate which is better. A reward model is trained on these preferences, and the LLM is then optimized to produce outputs that the reward model rates highly. RLHF is what transforms a text completion engine into a helpful, harmless assistant.

What LLMs Can Do

Modern LLMs demonstrate a broad range of capabilities:

• Text generation: Writing articles, emails, stories, poetry, and reports
• Question answering: Providing detailed answers to factual and analytical questions
• Code generation: Writing, debugging, and explaining code in dozens of programming languages
• Translation: Translating between languages with near-professional quality
• Summarization: Condensing long documents into concise summaries
• Reasoning: Solving logic puzzles, math problems, and multi-step reasoning tasks
• Analysis: Examining data, documents, and arguments for patterns and insights
• Conversation: Engaging in natural, multi-turn dialogue on virtually any topic

A key property that makes LLMs so versatile is in-context learning: the ability to learn new tasks from examples provided in the prompt, without any parameter updates. Show the model a few examples of a classification task, and it can classify new inputs -- a capability that was not explicitly trained.

Limitations and Challenges

Despite their impressive capabilities, LLMs have significant limitations that users and developers must understand:

Hallucination

LLMs sometimes generate confident-sounding text that is factually incorrect. They can fabricate citations, invent statistics, and create plausible but false narratives. This happens because the model optimizes for text that sounds likely, not for truth.

Knowledge Cutoff

LLMs only know what was in their training data. They have no awareness of events after their training cutoff date and cannot access the internet or real-time information unless specifically augmented with such capabilities.

Context Window Limits

Every LLM has a maximum context window -- the total number of tokens it can process in a single conversation. While modern models have expanded this dramatically (from 4K to 200K+ tokens), it still limits their ability to process very long documents.

Reasoning Failures

While LLMs can perform impressive reasoning, they can fail on problems that seem simple to humans, especially those requiring precise logical deduction, counting, or spatial reasoning. Their reasoning is pattern-based rather than truly logical.

LLMs are powerful tools, not infallible oracles. Understanding their limitations is as important as appreciating their capabilities.

The LLM Landscape

The LLM ecosystem has grown rapidly, with both proprietary and open-source options:

• OpenAI GPT series: GPT-4 and successors power ChatGPT, offering strong general capabilities
• Anthropic Claude: Emphasizes safety, helpfulness, and long context understanding
• Google Gemini: Multimodal from the ground up, processing text, images, video, and audio
• Meta LLaMA: Open-weight models that have catalyzed open-source LLM development
• Mistral: European company producing efficient, high-quality open models
• DeepSeek: Chinese AI lab producing competitive open-source reasoning models

The competition between these models drives rapid improvement in capabilities, efficiency, and safety. What was state-of-the-art six months ago may be surpassed by a model available for free today.

Why LLMs Matter

Large language models represent a fundamental shift in how humans interact with computers. Instead of learning specific software interfaces, users can describe what they want in natural language. Instead of writing code from scratch, developers can describe functionality and get working implementations. Instead of reading entire documents, professionals can ask questions and get targeted answers.

The economic impact is already substantial. LLMs are being integrated into every major software platform, from search engines and office suites to code editors and customer service systems. They are creating new categories of products and reshaping existing industries.

But the full implications of LLMs are still unfolding. As models become more capable, more efficient, and more accessible, they will continue to transform how knowledge work is done, how education is delivered, and how humans create and communicate. Understanding LLMs is not just valuable for technologists -- it is becoming essential for anyone who works with information.