What Is an LLM? Large Language Models Explained

An LLM (Large Language Model) is an AI model trained on large amounts of text data and used to understand or generate natural language. ChatGPT and OpenAI API models are representative ways to use LLMs from an application layer.[1] Understanding how LLMs work is the first step toward using modern generative AI effectively.

Before LLMs — A Brief History of Natural Language Processing

Before LLMs arrived, there were three main eras of approaches to making computers understand language.

Rule-based approaches (~1990s)

Humans manually defined grammar rules and dictionaries for computers to process. As rules multiplied, exceptions grew unmanageable, making practical quality hard to maintain.

Statistical methods (2000s)

Models learned the co-occurrence probabilities of words from large text corpora. Machine translation and speech recognition improved significantly, but understanding long-range context remained limited.

Neural networks (2010s onward)

Neural representation learning — such as Word2Vec — enabled word meanings to be encoded as numerical vectors. The 2017 introduction of the Transformer architecture opened the door to modern LLMs.[2]

What Is an LLM? — Three Defining Characteristics

An LLM has three core characteristics:

Characteristic	Description
Large	Contains billions to trillions of parameters
Language	Text data is the primary training target and output
Model	A collection of statistical patterns learned from data

The bar for “large” shifts with time. GPT-3 was reported as a 175-billion-parameter autoregressive language model and became a representative example of few-shot learning at large scale.[3]

How LLMs “Understand” Language

Step 1: Tokenize the text

LLMs don’t process raw text directly — they first split it into tokens (words, subwords, or symbols).

"Tokyo is the capital of Japan."
→ ["Tokyo", " is", " the", " capital", " of", " Japan", "."]

Token counts depend on the tokenizer used by the model, so practical work should verify counts with the tokenizer or API for the specific model.[1]

Step 2: Convert tokens to numerical vectors (embeddings)

Each token is converted into a high-dimensional numerical vector called an embedding. Words with similar meanings end up close together in vector space.

"king" - "man" + "woman" ≈ "queen"
(Semantic relationships are captured through vector arithmetic)

Step 3: Process context with the Transformer

The vectors are processed by a Transformer architecture. Through Self-Attention, it computes relationships between tokens to represent context.[2]

graph LR
    A["Input text"] --> B["Tokenization"]
    B --> C["Embedding\n(vectorization)"]
    C --> D["Transformer layers\n(Self-Attention)"]
    D --> E["Probability distribution\nover next token"]
    E --> F["Output text"]

Step 4: Predict the next token probabilistically

At its core, an LLM predicts the probability of what token comes next.

"Tokyo is the capital of" → What comes next?
- "Japan": 45%
- "Asia": 20%
- "the": 5%
- ...

The model samples from this distribution to pick the next token, then adds it to the input and predicts the next token again. This loop produces the complete response.

How LLMs Are Trained

Pre-training

The model learns from large text datasets using the “predict the next token” task. The GPT-3 paper reports pre-training on a mixture that included WebText-style data, Common Crawl, books, and Wikipedia.[3]

Instruction tuning

The pre-trained model is further trained to follow instructions — responding correctly to requests like “summarize this” or “translate this.”

RLHF (Reinforcement Learning from Human Feedback)

Human evaluators rate model outputs, and the model is updated via reinforcement learning to produce responses more aligned with human preferences. InstructGPT research used this approach to improve instruction following and human preference ratings.[4]

graph TD
    A["Pre-training\n(Learn language patterns from massive text)"]
    B["Instruction Tuning\n(Learn to follow instructions)"]
    C["RLHF\n(Improve quality with human feedback)"]
    A --> B --> C

LLM History and Current Model Specs

timeline
    title Key Milestones in LLM History
    2017 : Transformer paper is published
    2018 : BERT and early GPT-family research appear
    2020 : GPT-3 demonstrates few-shot learning
    2022 : ChatGPT is publicly introduced
    2020s : Multimodal and reasoning-oriented model use expands

Model names, context windows, input formats, pricing, and availability change frequently. For current specs, use each provider’s official model list.[1][5][6]

What to check	Source
OpenAI model availability, input formats, and API behavior	OpenAI Models / API docs
Claude model families and capabilities	Anthropic Claude models docs
Gemini model families and capabilities	Google Gemini API models docs

Limitations to Be Aware Of

LLMs are powerful but have important limitations.

Hallucination: LLMs sometimes state false information confidently. Because their core task is “predicting the next likely token,” they can generate fluent-sounding text without verifying facts.

Knowledge cutoff: Training data has an end date, so LLMs don’t know about events after their cutoff.

Reasoning limits: Complex math and multi-step logical reasoning can fail regardless of model family.

Context window limits: Each model has a maximum number of tokens it can process at once. Check current limits in the provider’s official model documentation.[1][5][6]

Summary

• An LLM is a large language model trained on massive text data, forming the foundation of modern AI assistants
• LLMs tokenize text, convert tokens to vectors, process context with the Transformer, and probabilistically predict the next token to generate text
• Training follows three stages: pre-training → instruction tuning → RLHF
• GPT, Claude, Gemini, Llama, and others each have different characteristics
• Understanding hallucination, knowledge cutoffs, and other limitations is essential for effective use

Frequently Asked Questions

Q: Are LLMs and ChatGPT the same thing?

A: No. “LLM” is the general term for large language models. ChatGPT is a chat service provided by OpenAI, and the available underlying models can change with OpenAI’s product and API offerings.[1]

Q: Do LLMs truly “understand” language?

A: This is philosophically debated. LLMs don’t understand meaning in the human sense — they learn statistical patterns (what token tends to follow what). But at the scale they operate, the result appears as human-like comprehension.

Q: Does more parameters always mean smarter?

A: Generally yes, but training data quality and quantity, and architecture efficiency, matter just as much. Research into smaller but highly capable “efficient models” is advancing rapidly.

Q: Can I run an LLM locally on my own computer?

A: Some models can. Required memory and speed depend on model size, quantization, inference engine, and hardware, so check the requirements for the specific model distribution and tool.

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References

1. OpenAI, Models
2. Ashish Vaswani et al., Attention Is All You Need, June 12, 2017
3. Tom B. Brown et al., Language Models are Few-Shot Learners, May 28, 2020
4. Long Ouyang et al., Training language models to follow instructions with human feedback, March 4, 2022
5. Anthropic, Claude models overview
6. Google AI for Developers, Gemini models