Boost your AI apps with domain-specific languages

We’re witnessing a fundamental shift in today’s business landscape as AI adoption transforms how companies operate across all sectors, from enhancing user-facing products with AI capabilities to remodeling internal workflows, and creating entirely new business models.

This shift presents intriguing possibilities, but also reveals important challenges in the communication with AI systems. As specialists in language engineering, we at TypeFox have naturally applied our expertise to AI systems. The result: solutions that give you precise control while unlocking AI’s full potential. In this article, we explain how we achieve this, focusing on the high-level approach.

Limitations of natural language in AI systems

The current AI revolution is largely driven by large language models (LLMs), which interact through plain text. Using natural language to control such systems is known as prompting.

When implementing solutions based on LLMs, several critical limitations consistently emerge in natural language interactions:

• Ambiguity: Words and phrases often have multiple interpretations, leading to unexpected AI responses that were not intended by the user (if the ambiguity is in the user prompt) or the system developer (if the ambiguity is in the system prompt).
• Verbosity: Natural language requires many words to express concepts that could be captured more concisely with structured approaches. This not only wastes time, but also leads to inefficiencies like context overload, a higher risk of hallucinations, and increased costs.
• Vagueness: Natural language’s non-strictness allows many ways to express identical concepts, making it challenging for language models to determine meaning. This leads to unpredictable behavior and makes the system harder to trust—especially in critical use cases.

We know that stable and reliable prompts do not fall from the sky, even if AI itself can help in generating good prompts. A single natural language word can affect output quality and consistency, creating substantial maintenance hurdles when switching between AI models or model versions.

Given these inherent limitations of natural language, we need a more structured approach that offers greater precision and reliability in AI communication. This is where domain-specific languages (DSLs) offer a compelling alternative.

Why DSLs transform AI communication

A domain-specific language is a specialized computer language designed for a particular application domain, with clear semantics and focused expressivity. DSLs have the potential to significantly enhance accuracy and maintainability in software systems.

Using DSLs for communication with LLMs provides key advantages:

• Precision: DSLs use exact domain terminology with clearly defined meaning. This eliminates ambiguity and ensures that the system behaves exactly as intended.
• Conciseness: DSLs allow experts to express complex ideas using minimal syntax. Since the structure is familiar to domain specialists, this lowers the cognitive burden and speeds up communication.
• Clarity: DSLs express intent explicitly, without relying on assumptions or hidden context. This makes the meaning easier to grasp—for both humans and language models.

These advantages have traditionally applied to human-to-human communication and automated tools (e.g. code generators), but they’re especially powerful when extended to bidirectional human-AI interactions.

Formal prompting through DSLs

One of the applications of DSLs that excite us most is improving how we prompt AI systems. Instead of relying solely on ambiguous natural language instructions, blending natural language prompts with DSLs guides AI behavior more precisely.

Consider this example of a natural language prompt:

Create a validation rule for loan applications based on financial criteria. Applicants with a higher income—say, above 75,000—and strong credit scores (typically from 720 upwards) should ideally be approved automatically.

For mid-range cases—for example, incomes around 50,000 combined with credit scores in the 650+ range—it may be safer to flag the application for manual review. Debt levels should also be taken into account; if someone’s debt-to-income ratio is too high, the application should probably be rejected.

It might also be helpful to distinguish between standard and high-priority manual reviews, depending on how close the applicant is to meeting the automatic approval thresholds.

Now compare it with a DSL-enhanced formal prompt:

Create a loan qualification validator using this business rule:

rule LoanEligibility
  when income >= 75000 AND creditScore >= 720 AND debtToIncomeRatio < 0.3
    then approve("automatic")
  
  when income >= 50000 AND creditScore >= 650 AND debtToIncomeRatio < 0.4
    then review("manual", priority: standard)
  
  when creditScore < 600 OR debtToIncomeRatio >= 0.5
    then reject("insufficient_qualifications")
  
  otherwise
    then review("manual", priority: high)
end

The DSL-enhanced formal prompt eliminates guesswork entirely. Where the natural language version loosely gestures toward “high income” and “probably too much debt”, the DSL spells out precise thresholds, categories, and actions in black and white. Each combination of income, credit score, and debt ratio leads to a clearly defined decision: approve, review, or reject.

Of course, the natural language prompt was intentionally a little vague (okay, maybe strategically fuzzy), just to make the contrast clear. In practice, human-written specs often strike a similar tone, blending hard facts with soft edges. That’s exactly where DSLs shine: they bring structure and precision to ideas that are otherwise open to interpretation, and can automatically flag input that is incomplete or inconsistent.

By encoding business rules directly in a structured format, DSLs act like a pair of glasses for the AI—suddenly the blurry becomes crystal clear. The result? More predictable outputs, fewer misfires, and code that behaves exactly as intended.

Human readable structured results from AI

DSLs excel at structuring LLM outputs. While JSON and YAML are commonly used for structured outputs, they often become unwieldy for complex data or expressions. DSLs frequently provide more intuitive and domain-appropriate representations that maintain the machine-readable nature of structured data while offering a better user experience by being easier to understand.

Consider the following structured output representing a logistics rule in JSON:

{
  "type": "WaitUntilStatement",
  "condition": {
    "operator": "<",
    "left": {
      "operator": "*",
      "left": {
        "object": { "reference": "warehouse" },
        "property": "stockLevel"
      },
      "right": { "reference": "demandForecast" }
    },
    "right": { "reference": "reorderThreshold" }
  }
}

While structurally complete and machine-readable, this format is verbose and difficult to grasp at a glance. A domain-specific language (DSL) can express the same logic far more readably:

wait until (warehouse.stockLevel * demandForecast) < reorderThreshold

This variant retains the same structure and semantics, but in a concise, intuitive form that’s easier for humans to read, write, and review—without sacrificing the precision needed for machine processing.

The DSL approach provides greater clarity through domain-specific syntax and terminology that matches how practitioners naturally think about their problems. As organizations build systems where AI increasingly produces structured data and logic, purpose-built DSLs benefit everyone in the workflow, not just developers. They reduce cognitive load by abstracting away generic syntax details and focusing attention on the domain concepts that matter to business analysts, domain experts, end users, and other stakeholders.

Additionally, DSLs can enforce semantic constraints at the language level, catching domain-specific errors that generic formats like JSON cannot detect without additional validation layers. This built-in validation not only ensures higher quality outputs but makes AI-generated content immediately more accessible to domain experts and more useful in production environments where reliability is critical.

Semiformal prompting: the best of both worlds

The real power emerges when combining DSLs with natural language in what we call the semiformal approach. Every DSL already has ways to incorporate natural language through:

1. Identifiers: Descriptive symbol names that carry semantic meaning (symbols are variables, functions, or any other named declarations)
2. Comments: Free-form natural language explanations placed outside the formal structure
3. Strings: Natural language content embedded within the structure, often used for labels, messages, or domain-specific annotations that are part of the logic but not interpreted formally
4. Documentation: Supporting materials and examples that aid in understanding and applying DSL components effectively

These elements transport essential semantic information that helps both humans and AI understand the meaning within formal structures as well as the user’s intent expressed in natural language.

Traditional tools only process the formally defined parts of a DSL—symbol names mean nothing to compilers, which is why code obfuscation works. Language models, however, can understand both formal and informal elements.

// Product grid for online fashion store
component ProductGrid {
  
  container: flexbox(wrap: true, gap: 16px) {
    
    ProductCard foreach $products {
      image: $product.hero_image
      title: $product.name
      price: $product.current_price
      rating: stars($product.avg_rating)
      
      styling: "Large product images - customers buy with their eyes first"
      
      hover_effects: {
        transform: scale(1.02)
        transition: "Smooth but quick - customers browse rapidly"
      }
      
      WishlistButton {
        position: top-right
        style: "Subtle heart icon - don't compete with image, name, price"
      }
    }
  }
  
  filters: SidebarFilters {
    categories: $available_categories
    price_range: slider($min_price, $max_price)
    colors: color_swatches($available_colors)
    
    layout: "Collapse on mobile but keep key filters visible"
  }
  
  pagination: "Infinite scroll - maintain shopping momentum"
}

This semiformal prompting example shows how natural language within the DSL captures design intent. The formal DSL structure defines the component hierarchy and data bindings, while natural language parts provide essential UX guidance that both humans and LLMs can understand and that pure technical specifications cannot capture in a concise way. This semiformal combination offers the best of both worlds in practical applications.

While the examples we’ve explored highlight specific domains, the approach itself is universally applicable. Domain-specific languages are not confined to a handful of use cases—they can be tailored to any field where precision, structure, and human-machine understanding are vital. From finance and healthcare to manufacturing and education, DSLs unlock more robust, interpretable, and maintainable AI solutions across the board.

Engineering the next evolution in AI communication

As AI systems grow more sophisticated, the need for structured communication interfaces becomes increasingly evident. Domain-specific languages offer a proven technical approach to address the precision and clarity challenges that natural language prompts cannot solve alone.

Technical teams that integrate these language engineering principles into their AI workflows gain substantial benefits:

• More predictable LLM outputs with greatly reduced maintenance overhead
• Clearer separation between domain logic and natural language components
• Enhanced capabilities for validation, testing, and formal verification

For organizations building business-critical AI applications, these aren’t merely optimizations but foundational requirements for production-grade systems.

The tools and patterns for semiformal human/AI interaction exist today. Whether you’re enhancing existing AI applications or building new ones, incorporating DSL principles provides the technical rigor your AI systems require.

Ready to apply language engineering to your AI challenges? Let’s develop the precise interfaces your applications need.

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TypeFox is a team of highly specialized software developers pioneering the field of language engineering. We build the tools that help companies around the world create domain-specific languages and interactive environments for their unique needs. Want to see how semiformal approaches could transform your AI applications? Get in touch.