DSLs or LLMs — Why Use a Formal Language?
Large Language Models have changed how people think about expressing logic, rules, and contracts. Yet, a well-designed Domain-Specific Language (DSL) with a real parser remains the foundation for systems that demand determinism, auditability, and trust. Rather than competing, LLMs and DSLs complement each other: the model can author or explain, while the DSL guarantees structure and safety.
Why a DSL Still Matters
A DSL defines a fixed syntax and semantics for expressing rules, calculations, or agreements. This makes it possible to parse, validate, and execute instructions predictably—something free-form language cannot guarantee.
Determinism and Safety
A parser such as ANTLR enforces clear structure. Every token has meaning, and no instruction can slip through as “sort-of valid.” This is essential for domains like finance, contracts, or policy enforcement, where outcomes must be exact and reproducible.
Auditability and Governance
DSLs produce artifacts that can be versioned, diffed, reviewed, and tested like code. They’re explainable, traceable, and safe to evolve over time. This supports formal governance processes and compliance reviews.
Performance and Execution
Once parsed into an abstract syntax tree (AST), a DSL executes faster and more consistently than any interpretation of natural text. ASTs can also be serialized or composed into other systems—critical for workflows, simulations, or pricing engines.
Tooling and Integration
Grammars enable syntax highlighting, refactoring, and static analysis. They open the door to IDE support, linters, documentation generators, and automated migrations—tools that only exist because the language is well defined.
Where LLMs Excel
LLMs thrive as assistants around a DSL:
- Authoring: transforming natural language into DSL statements.
- Explaining: turning formal clauses into readable summaries.
- Refactoring: applying small intent-based edits (“make this payment contingent on revenue”).
- Debugging: interpreting parse errors and suggesting fixes.
- Pattern discovery: mining existing DSL corpora to propose reusable examples.
In short, the LLM becomes a collaborator for humans, not a replacement for the formal language layer.
The Hybrid Model
The most robust architectures combine both worlds:
- Authoritative DSL — the single source of truth, parsed deterministically.
- AST Schema — a versioned, typed structure for storage and integration.
- LLM Interface — a natural-language front end that compiles to or from the DSL.
- Validation Pipeline — automatic parsing, semantic checks, and linting.
- Round-trip Consistency — DSL ⇄ AST ⇄ natural language transformations that preserve meaning.
This pattern preserves the rigor of traditional language design while enabling conversational authoring and explanation.
When to Use Each
| Context | Recommended Approach |
|---|---|
| Financial, contractual, or policy logic | DSL + parser |
| Systems needing version control and diffing | DSL |
| Interactive natural-language input | LLM front end + DSL backend |
| Low-risk or experimental utilities | LLM only |
| Frequently changing rule sets | DSL with LLM-assisted editing |
Design Principles for Modern DSLs
- Keep the grammar compact and orthogonal.
- Push complexity into named functions or intrinsics.
- Provide a semantic validation layer beyond syntax.
- Use explicit versioning (e.g.,
model 1.2.0). - Implement a linter with human-readable feedback.
- Expose the schema so that LLMs can generate valid output directly.
The Rationale
Natural language is expressive but ambiguous. DSLs are constrained but precise. LLMs bridge the two, helping people write and interpret structured expressions without giving up correctness. The right design choice isn’t DSL or LLM—it’s DSL plus LLM, each doing what it does best.