This research introduces type-constrained decoding for LLMs to generate code that respects language-specific type systems, significantly reducing compilation errors.

• Enhances code generation by forcing LLMs to adhere to formal typing rules
• Implements a novel type-guided sampling approach that works across multiple programming languages
• Demonstrates substantial reductions in type errors while maintaining code quality
• Improves both compilation success rates and functional correctness

For engineering teams, this advancement means more reliable AI coding assistants that produce cleaner, more secure code with fewer runtime errors—potentially reducing development time and technical debt.

Type-Constrained Code Generation with Language Models