A minimalist DSL to enforce deterministic code generation with LLMs

A developer has created IPL (Intent Programming Language), a minimalist DSL designed to enforce deterministic code generation with LLMs by constraining the expression space. The language uses a reduced grammar and separates business logic into an auditable intermediate artifact, enabling any basic or local LLM to translate natural language into .ipl files zero-shot and transpile them to multiple targets including Rust, Go, Java, Python, JavaScript, TypeScript, and HTML. The approach aims to reduce development costs and cycles by preventing hallucinations and architectural over-engineering.

Hi everyone, When integrating LLMs into production pipelines, we often face stochastic issues: hallucinations, architectural over-engineering, and a lack of reproducibility. Instead of stacking layers of fragile prompt engineering, I approached this from a language design perspective: constraining the expression space to enforce determinism. I designed IPL Intent Programming Language with a pragmatic goal: making software generation reliable while drastically cutting development costs and cycles. To build this minimalist DSL, I leveraged an LLM to help identify and structure a universal, irreducible core vocabulary designed to serve as a deterministic bridge between human intent and executable code. Minimal Grammar: A strictly reduced syntax with core action primitives and standardized types. The structure is lightweight enough that any basic or local LLM can translate natural language into an .ipl file zero-shot. Separation of Concerns: Business logic is isolated in a human-readable, auditable intermediate artifact, completely decoupled from the target tech stack. Multi-File & Heterogeneous Stacks: Multi-file .ipl support allows orchestrating complex architectures mixing different backend and frontend layers. Constrained Transpilation: By locking the lexical scope, the LLM cannot hallucinate exotic architectures. It acts purely as a reliable transpilation engine. Natural Language Intent: “If the order exceeds 100 €, apply a 10% discount and notify the customer.” Intermediary Intent Contract .ipl : Generated by a basic LLM or written manually read order.total as number if order.total 100: compute discount = order.total 0.10 send notification to order.customer id Target Code Generation: From this validated contract, the model transpiles deterministic, idiomatic code across various targets — backend Rust, Go, Java, Python , frontend/integration JavaScript, TypeScript, HTML , or hybrid stacks. The repository includes the specification, parser logic, and the Studio IDE interface: I would love to hear your thoughts, feedback, or critiques on this constraint-first approach to LLM-driven development