Large Language Model (LLM)

LLMs are transformer-based neural networks trained through a process called self-supervised learning. During training, the model learns to predict the next token in a sequence, absorbing patterns in grammar, logic, code syntax, and reasoning. After pre-training on trillions of tokens, the model is fine-tuned with human feedback (RLHF) to follow instructions, refuse harmful requests, and produce helpful output. The result is a system that can write code, explain concepts, debug errors, and reason about complex problems.

LLMs transformed software development because they understand code at a semantic level—not just syntax. They can read a function and explain what it does, identify bugs by reasoning about logic, translate between programming languages, and generate implementations from natural language descriptions. This capability powers every AI coding tool: code completion, code review, refactoring assistants, and autonomous coding agents.

The model behind a tool matters more than the tool itself. Claude Code's effectiveness comes from Claude's reasoning capabilities. When evaluating AI coding tools, pay attention to which LLM they use and how they leverage it.