Generating DSL Code with LLMs (elijahpotter.dev)

A recent experiment exploring the capabilities of large language models (LLMs) revealed their potential to generate code for domain-specific languages (DSLs). The creator of Weir, a programming language aimed at correcting natural language, tested whether an LLM could effectively handle Weir’s syntax and the semantics of English, while validating its output through tests. The results were promising; an instance of GPT 5.2 Instant produced accurate Weir rules for correcting common language mistakes like "as nauseam" and handling complex double-negative structures in seconds, demonstrating quicker and more efficient rule generation compared to traditional programming languages like Rust. This experiment holds significant implications for the AI/ML community, showcasing how LLMs can adapt to and effectively utilize novel languages and DSLs. The successful generation of rules without needing an extensive codebase suggests that LLMs can generalize their knowledge beyond well-known languages, making them valuable tools for businesses looking to enforce style consistency in communication. The exploration also hints at a growing capability for LLMs to contribute creatively within the programming space, encouraging further experimentation and potential applications across various sectors.