Code Generation

Code generation is what makes vibe coding possible. AI models trained on millions of code repositories can produce working implementations from descriptions — transforming how software gets built.

How Code Generation Works

Modern code generation relies on large language models that:

1. Understand context — Your description, existing code, project structure
2. Predict patterns — What code typically follows your setup
3. Generate tokens — Produce code character by character
4. Self-correct — Adjust based on syntax and logic rules

Quality Factors

Generated code quality depends on:

• Prompt clarity — Specific descriptions get better results
• Context provided — More relevant context = better code
• Model capability — Newer models generally perform better
• Task complexity — Simple tasks have higher success rates

What AI Generates Well

• CRUD operations and API routes
• UI components from descriptions
• Data transformations and utilities
• Test cases from implementations
• Documentation from code

What Still Needs Human Judgment

• Novel algorithms
• Complex business logic
• Security-critical code
• Performance optimization
• Architectural decisions

Code generation is a tool, not a replacement for understanding. The best results come from developers who can guide AI effectively and evaluate its output critically.