LLM (Large Language Model)
LLM & Language ModelsA type of AI model trained on massive amounts of text data that can understand, generate, and reason about human language. GPT-4, Claude, Gemini, and Llama are all LLMs.
Large Language Models are the technology behind the AI revolution that started with ChatGPT in late 2022. An LLM is a neural network trained on billions of words from the internet, books, code, and other text sources, enabling it to generate human-like text, answer questions, write code, translate languages, and reason through complex problems.
The 'large' in LLM refers to the number of parameters (learned values) — ranging from a few billion (Llama 8B) to hundreds of billions (GPT-4, estimated at 1.8 trillion). More parameters generally mean more capability, but also more expensive to run.
The major LLM families are: GPT (OpenAI), Claude (Anthropic), Gemini (Google), Llama (Meta, open-source), and Mistral (Mistral AI, open-source). Each has different strengths — Claude excels at long-form analysis, GPT-4 at breadth, Gemini at multimodal tasks, and Llama at local deployment.
Real-World Example
An LLM predicts the next token. Everything else it appears to do — reasoning, summarising, answering — is that one operation applied at scale.
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What is LLM (Large Language Model)?
A type of AI model trained on massive amounts of text data that can understand, generate, and reason about human language. GPT-4, Claude, Gemini, and Llama are all LLMs.
How is LLM (Large Language Model) used in practice?
An LLM predicts the next token. Everything else it appears to do — reasoning, summarising, answering — is that one operation applied at scale.
What concepts are related to LLM (Large Language Model)?
Key related concepts include Token, Context Window, Transformer, Foundation Model, Pre-training, Fine-tuning. Understanding these together gives a more complete picture of how LLM (Large Language Model) fits into the AI landscape.