#3894 Gen-AI and DSLs

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#3894 Gen-AI and DSLs

#3894 Discuss how to create domain specific languages with generative-AI

Generative AI has fundamentally changed DSL engineering by serving two distinct roles: it acts as a language architect (helping you design and build the compiler/interpreter) and as a competent user (writing valid code in your DSL for end-users).

The following guide details how to create a DSL using generative AI, moving from abstract design to a reliable, production-ready system.

Phase 1: Design & "Bootstrapping" the Language

Instead of manually writing formal grammars from scratch, you can use an LLM to iteratively design the syntax based on the concepts you want to express.

  • Syntax Discovery: Describe your domain concepts to an LLM (e.g., "I need a language for defining smart home automation rules involving sensors, triggers, and actions") and ask it to propose three different syntax styles (e.g., Pythonic, SQL-like, or natural language style).

  • Grammar Generation: Once you pick a syntax, ask the LLM to formalize it into a standard grammar format like EBNF (Extended Backus–Naur Form) or an ANTLR grammar file.

    • Prompt: "Convert the smart home syntax we discussed into a formal EBNF grammar compatible with the Python Lark parser."

  • Iterative Refinement: Paste the generated grammar back into the LLM and ask it to "act as a parser." Feed it edge-case examples to see if it interprets them correctly. If it fails, ask it to correct the grammar.

Phase 2: Building the Runtime (Parser & Interpreter)

You can use the LLM to write the actual software that executes your language.

  • Parser Generation: If you have a grammar (from Phase 1), prompt the LLM to generate the parsing code.

    • Tool Recommendation: Lark (Python) is excellent for this because it consumes EBNF directly.1

    • Prompt: "Write a Python script using the lark library that parses this EBNF grammar. Include a Transformer class that converts the parse tree into a JSON object."

  • Interpreter Logic: Ask the LLM to write the visitor pattern or interpreter logic that executes the parsed instructions.

    • Prompt: "Write a Python class that takes the JSON output from our parser and executes the logic. For the 'turn_on_light' action, just print a confirmation message for now."

Phase 3: Ensuring Reliability (Constrained Generation)

This is the most critical step for production. If you want an LLM to write code in your DSL for users (e.g., a user types "Turn on lights when I enter," and the AI writes the DSL code), you cannot rely on simple prompting. You must use Constrained Decoding.

Standard LLMs are probabilistic and may "hallucinate" syntax errors (e.g., inventing a function dim_lights that doesn't exist).2 Constrained decoding forces the LLM to select only tokens that are valid according to your grammar.3

  • Tools to Use:

    • Outlines / Guidance / Instructor: Python libraries that enforce structured output.4

    • Llama.cpp Grammars: If running local models (like Llama 3 or Mistral), you can pass a .gbnf file (a grammar format) that strictly forbids the model from outputting invalid syntax.5

  • Workflow:

    1. Define your DSL schema (e.g., using Pydantic models or a Regex).

    2. Pass this schema to the library (e.g., outlines.generate.json(model, schema)).6

    3. The LLM is now mathematically incapable of generating a syntax error; it will always output valid DSL code.7

Phase 4: "LLM-Hardened" Design

When creating a DSL in the AI era, you should design the language for the AI, not just for humans.

  • Avoid Ambiguity: AI struggles with languages where context changes the meaning of a symbol heavily (like Perl). Prefer verbose, explicit keywords.

  • Embed Descriptions: Include "comment" fields in your DSL structure. This allows the LLM to explain its "chain of thought" inside the generated code before writing the actual logic, which significantly improves logic accuracy.

  • Reduction to JSON/YAML: If possible, design your DSL as a strict subset of JSON or YAML. LLMs are already heavily pre-trained on these formats, making them much better at generating them than custom bracket-based syntaxes.

Summary Workflow: From Idea to Execution

StepActionTools
1. DefinePrompt LLM to design syntax for your domain.ChatGPT / Claude
2. FormalizeConvert syntax to EBNF or JSON Schema.LLM + Manual Review
3. ImplementGenerate a Parser/Interpreter script.Python Lark or PLY
4. ConstrainSet up a "Text-to-DSL" generator using grammar constraints.Outlines / Guidance
5. DeployUser speaks natural language -> LLM generates valid DSL -> Interpreter executes it.Your App

Video Resource

... LLMs, DSLs, and the Art of Generating Generators

This video features a discussion on "Generating the Generator," covering how to use AI to build DSLs that automate development workflows, which directly aligns with the "meta" aspect of your request.


How can Mathematica supported by Wolfram LLM Kit assist in DSL development?

Mathematica, combined with the Wolfram LLM Kit, can accelerate Domain-Specific Language (DSL) development by acting as a semantic bridge between natural language and your DSL's formal syntax.

Instead of writing a rigid parser from scratch immediately, you can use the LLM Kit to prototype the grammar, generate reference implementations, and eventually build a "fuzzy" interpreter that allows users to speak to your DSL.

1. Prototyping the DSL Syntax (The "Fuzzy" Compiler)

Before locking down a formal grammar (like BNF), you can use LLMFunction to experiment with how you want the language to look. You can treat the LLM as a "soft compiler" that translates natural language intent into your proposed DSL syntax.

  • Concept: Create a prompt that describes your hypothetical DSL and asks the LLM to write code in it.

  • Wolfram Tool: LLMFunction or ChatNotebook.

  • Workflow:

    1. Define a "Function Prompt" that includes a few examples of your desired DSL.

    2. Pass natural language descriptions of problems to this function.1

    3. Review the output to see if the syntax is expressive enough before you build the actual parser.

Example:

(* Define a prototype translator for a Robot Control DSL *)
robotDSLWriter = LLMFunction[
  "Translate the following natural language command into a code block 
  using a DSL with syntax like: MOVE(distance, unit), TURN(degrees). 
  Input: `input`",
  "Input" -> "Move forward 10 meters then turn left."
]

2. Semantic Parsing & Translation (Natural Language -> DSL)

Once your DSL is defined, you can use the LLM Kit to build a robust front-end that translates user intent into valid DSL code. This allows non-experts to use your DSL.

  • Concept: Use "Few-Shot Learning" to teach the LLM your grammar rules without fine-tuning a model.

  • Wolfram Tool: LLMExampleFunction.

  • How it works: You provide a list of Input -> Output examples (Natural Language -> Your DSL). The function automatically constructs a prompt that teaches the LLM how to perform this specific translation.

(* Create a translator from examples *)
dslTranslator = LLMExampleFunction[{
   "Initialize system" -> "SYS_INIT();",
   "Set voltage to 5V" -> "SET_VOLT(5.0);",
   "Read sensor 1" -> "READ_SENSOR(1);"
}]

(* Use it to generate code for new commands *)
dslTranslator["Check sensor 2 and set voltage to 3V"]
(* Likely Output: READ_SENSOR(2); SET_VOLT(3.0); *)

3. Hybrid Parsing & Error Recovery

Traditional parsers (built with CodeParser or similar) are brittle; they fail completely on a missing semicolon. You can use the LLM Kit to handle "error recovery" or "fuzzy parsing."2

  • Workflow:

    1. Attempt to parse user input with your standard deterministic parser.

    2. If it fails (FailureQ), pass the invalid code and the error message to an LLMFunction.

    3. Ask the LLM to "fix the syntax based on these valid examples" and return the corrected code.

4. Interactive Documentation via LLMTool

You can turn your DSL documentation into an active agent. By wrapping your DSL's specification or documentation in an LLMTool, you allow the LLM to query your docs to answer user questions or write code.

  • Wolfram Tool: LLMTool.3

  • Application:

    • Create a tool named system_dsl_docs that contains text from your documentation.

    • When a user asks "How do I loop in this language?", the LLM calls the tool, reads your looping syntax, and explains it to the user with a code example.

5. Verified Code Generation (The "AntonAntonov" Approach)

Community packages like AntonAntonov/DSLTranslation (available in the Paclet Repository) demonstrate a mature pattern for this.4 They often use a "Pipeline" approach:

  1. Translation: LLM translates Natural Language -> Abstract Syntax Tree (AST) or DSL Code.

  2. Verification: Mathematica evaluates the AST to ensure it is structurally valid.

  3. Execution: If valid, the DSL code is executed; if not, the error is fed back to the LLM for a retry.

Summary of Tools for DSL Dev

Development PhaseWolfram LLM Kit FeatureUsage
DesignChatNotebookBrainstorm syntax ideas and see "hallucinated" examples of how it might look.
ParsingLLMExampleFunctionCreate a robust "Natural Language to DSL" translator using examples (few-shot).
ValidationLLMFunction + CodeParserUse LLM to fix syntax errors that standard parsers reject.
SupportLLMToolGive the LLM access to your DSL's documentation to act as a coding assistant.

Relevant Video

This video showcases the "Wolfram Prompt Repository," which is essential for finding pre-made prompts (like "CodeWriter" or "CodeReformat") that can be adapted for your DSL generation tasks.

The Wolfram Prompt Repository

The video is relevant because it explains how to use and create "Prompt resources" that can package your DSL's rules into reusable components for the LLM Kit.


From <https://gemini.google.com/app/>  Google Gemini 3.0 Pro (Thinking)

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