[MS] AI Coding Agents and Domain-Specific Languages: Challenges and Practical Mitigation Strategies - devamazonaws.blogspot.com

1. Introduction

AI coding agents/assistants such as GitHub Copilot have become common in modern software engineering workflows. Their strengths—rapid pattern completion, context-aware suggestions, and the ability to learn style from local code—stem from broad training on large corpora of public, general-purpose code. They perform best when the languages, libraries, and idioms requested by developers align with patterns they have seen many times before. Domain-Specific Languages (DSLs) break this assumption. DSLs are deliberately narrow, domain-targeted languages with unique syntax rules, semantics, and execution models. They often have little representation in public datasets, evolve quickly, and include concepts that resemble no mainstream programming language. For these reasons, DSLs expose the fundamental weaknesses of large language models when used as code generators. While AI coding agents excel at generating code for mainstream languages, recent research shows their accuracy on domain-specific languages (DSLs) often start below 20% as a direct result of limited training exposure and missing domain context. The research also shows that with targeted interventions, such as injecting curated examples and explicit domain rules, these agents can achieve accuracy rates of up to 85%, approaching their performance on well-supported languages. This paper outlines the core challenges AI agents like GitHub Copilot face with DSLs, then provides practical mitigation strategies.

2. Why DSLs Are Difficult for AI Coding Agents

2.1. Minimal Training Exposure

Language models rely on statistical pattern recognition. When a DSL has little or no presence in the model’s training data, the model has:

• No syntax blueprint
• No idioms to mimic
• No grounding in the domain’s concepts
• No corpus from which to infer correct API usage

The result is predictable: the model guesses. It synthesizes constructs from similar-looking languages or invents functions and keywords that do not exist.

2.2. DSL Syntax Divergence

DSLs often feature:

• Non-C-like control structures
• Custom assignment operators
• Declarative or dataflow semantics
• Specialized type systems
• Embedded domain metadata

These features violate assumptions baked into mainstream programming patterns. Without enough examples, Copilot cannot reliably derive rules governing DSL grammar or symbol resolution.

2.3. Confabulated Semantics

When unable to recall or infer a correct symbol, the LLM frequently:

• Fabricates APIs
• Substitutes concepts from unrelated languages
• Conflates the DSL with a visually similar syntactic family
• Misattributes behavior or domain rules

These errors are especially common in DSLs where the domain logic is abstract, such as game logic, infrastructure orchestration, shader languages, or policy languages.

2.4. Missing Schema, Types, or Tooling Signals

General-purpose languages benefit from rich ecosystems:

• Type definition files
• Language servers
• Linters
• IntelliSense
• Compiler error messages

Many DSLs, especially new ones, lack mature Language Server Protocol (LSP) support, which provide syntax and error highlighting in the code editor. Without structured domain data for Copilot to query, the model cannot check its guesses against a canonical schema.

3. Practical Mitigation Strategies

Because the problem stems from missing knowledge and structure, the solution is to supply knowledge and impose structure. Copilot’s extensibility, particularly Custom Agents, project-level instruction files, and Model Context Protocol (MCP) make this possible. Below are strategies applicable to any DSL.

3.1. “Onboard” the AI: Establish Explicit Domain Context

When introducing complex DSL logic, consider starting with pseudocode or a familiar language implementation, then asking Copilot to translate with explicit DSL syntax guidance. This leverages Copilot's stronger training in mainstream languages as a bridge to your DSL. AI coding agents behave like a new engineer with no background in your language. Like we would for any developer on our team, we should provide:

• Syntax rules
• Structural constraints
• Naming conventions
• Domain concepts
• Mapping to any analogous industry standards and terms (if existing)
• Valid usage examples
• Forbidden constructs

This can be done through:

GitHub Copilot Custom Agents

GitHub Copilot’s custom agents enable you to configure the AI to adopt different personas tailored to specific development roles and tasks. Each persona can have its own behavior, available tools, and instructions. Define a persistent DSL-aware persona that governs all chat/agent interactions. Include:

• A concise grammar overview
• Correct and incorrect examples
• Operation semantics
• Domain constraints
• Examples of invalid language behaviors

Repository-level Instructions (copilot-instructions.md)

Copilot automatically reads the copilot-instructions.md file. Use it to permanently encode or reference DSL information:

• “How to write code in this DSL”
• Syntax and idioms
• Domain dos and don’ts
• Examples of canonical patterns
• References for online DSL documentation, if available

This approach gives Copilot the scaffolding it does not get from its training data. Structure matters: AI systems chunk documentation for retrieval. Keep related information proximate – constraints mentioned three paragraphs after a concept may never appear in the same retrieval context. Each section should be self-contained with necessary context included.

3.2. Seed the Workspace with High-Quality DSL Examples

Copilot is strongly influenced by context from open files. Even a small number of well-formed DSL samples dramatically anchors its completions. Research demonstrates 3-5 well-commented examples optimize performance – fewer lacks context; more creates noise. Microsoft's DSL-Copilot stores examples as “prompt + additionalDetails + correct response” pairs. Recommended content:

• Minimal reference implementations
• Short “golden path” example scripts
• Idiomatic patterns with comments
• Canonical naming/structuring conventions

This is the fastest and highest-impact mitigation. Copilot imitates what it sees.

3.3. Use a Compiler or Validator in the Loop

Because DSL output will initially be noisy:

• Generate code with Copilot
• Run it through the DSL compiler/linter or a language server (LSP) provided by an extension
• Feed the errors back into Copilot Chat
• Request correction using actual compiler feedback

This mirrors the workflow demonstrated in Microsoft’s DSL-Copilot example: the LLM generates DSL code, the DSL’s compiler or parser validates that code, and any resulting errors are fed back into the model for correction. The process is repeated until the output is syntactically valid and semantically acceptable.

3.4. Use Extensibility to Inject Domain Schema: The Bicep Example

Azure Bicep is a good example of improving AI performance for a DSL. Microsoft exposed Bicep’s type system and resource schema through an MCP (Model Context Protocol) server (Azure MCP) and provides language server validation through a VS Code extension. From the MCP server, Copilot can query:

• Valid resource types
• Allowed properties
• Type signatures
• Constraints

This anchoring eliminates confabulation (sometimes called “hallucinations” with LLMs) because the model is grounded in real domain definitions. Microsoft's DSL-Copilot project found LLMs still confabulate even with grammar files provided, though they format responses in correct structure, validation remains essential.

Summary

When a DSL has an accessible schema, type system, or API surface, expose it to the model—via MCP, custom agents, or structured documentation injected into instructions. Even if your DSL lacks a formal schema, you can approximate:

• A hand-crafted “type sheet”
• A list of valid functions
• A catalog of language constructs
• Allowed/forbidden operators
• State machine diagrams or dataflow patterns

Providing this structured domain metadata raises Copilot’s accuracy significantly.

4. Conclusion

AI coding agents are powerful, but they are pattern-driven tools. DSLs, by definition, lack the broad pattern exposure that enables LLMs to behave reliably. The solution is to provide the model with:

1. Explicit DSL context: syntax rules; naming conventions
2. Curated examples to anchor completions
3. Structured instruction files for consistency, e.g., custom agents and repository instruction files
4. Compiler and LSP validation loops
5. Schema anchoring and extensibility mechanisms, e.g., VS Code extensions and MCP servers
6. Pseudocode translation bridges for complex logic

Azure Bicep demonstrates that when a DSL’s schema is made machine-readable and validated via the LSP, AI coding agents can become impressively accurate. The broader thesis is simple: AI coding agents do not inherently understand DSLs, but they can become highly effective once you supply the rules, patterns, and domain metadata the model was never trained on, reinforced with validation mechanisms.
Post Updated on December 18, 2025 at 06:36PM
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