LLM (Large Language Model)
AI models trained on vast amounts of text that can understand and generate human-like language.
Large Language Models (LLMs) are the technology behind modern AI tools. They're neural networks trained on enormous datasets of text—including books, websites, and code repositories.
How they work: LLMs learn patterns in language by predicting the next word in a sequence. Through training on billions of examples, they develop an understanding of grammar, facts, reasoning, and even coding patterns.
Key characteristics: - Scale: Modern LLMs have billions of parameters (GPT-4 is estimated at 1+ trillion) - Context: They can consider thousands of tokens of context when generating responses - Versatility: The same model can write code, explain concepts, translate languages, and more
Popular LLMs for coding: - GPT-4: OpenAI's flagship model, excellent at coding and reasoning - Claude: Anthropic's model, known for longer context and thoughtful responses - Llama: Meta's open-source model family - Codex: OpenAI's code-specific model (powers GitHub Copilot)
Understanding LLMs helps you use AI tools more effectively. These models don't "understand" code the way humans do—they pattern-match based on their training data.
Examples
- •GPT-4 powering ChatGPT and many coding assistants
- •Claude providing AI capabilities in Cursor
- •Llama running locally on developer machines