Code generation AI produces functional code from natural language descriptions, enabling non-programmers and accelerating developers. Capabilities: Function implementation, algorithm coding, boilerplate generation, test writing, code completion, full application scaffolding. Leading models: GPT-4/Claude (general), Codex (OpenAI), CodeLlama, StarCoder, DeepSeek-Coder, Gemini. Specialized training: Pre-train on code repositories (GitHub), fine-tune on instruction-code pairs, RLHF for code quality. Key techniques: Fill-in-the-middle (FIM), long context for repository understanding, multi-file editing. Evaluation benchmarks: HumanEval, MBPP, MultiPL-E, SWE-bench (real GitHub issues). Integration: IDE extensions, CLI tools, API services, autonomous coding agents. Use cases: Rapid prototyping, learning new languages, boilerplate automation, code translation, documentation to implementation. Best practices: Review all generated code, provide context, iterate on prompts, test thoroughly. Limitations: Can produce plausible but incorrect code, security vulnerabilities, over-reliance on training patterns. Transforming software development with augmented productivity.