How to Build Language‑First APIs: 5 LLM‑Powered Architectural Patterns

Pattern: Natural Language as DSL

Intent: Use natural language as a domain‑specific language (DSL) to describe system requirements. Extract key fields (e.g., role, action, value) with regular expressions or LLM parsing and validate them.

Example: A user‑story template "As a <role>, I want to <action>, so that <value> " can be parsed into separate components. After extraction, a regex can verify:

role = 购买了商品的用户 action = 退货 value = 在不满意或商品存在问题的情况下获得处理 These components become concrete acceptance criteria, improving development efficiency.

Pattern: Real‑time Streaming DSL

Intent: Return results incrementally so that the client can render updates while the model continues processing.

Typical scenario: Large data‑processing tasks where a single JSON payload would take many seconds. Streaming partial fragments (markdown tables, CSV, JSONP) keeps the UI responsive.

Implementation tip: Use HTTP chunked transfer or WebSocket; each chunk should be a self‑contained fragment that can be parsed independently.

Pattern: DSL‑Guided Function Generation

Intent: Combine a DSL that encodes structural constraints with an LLM to generate text that obeys those constraints.

Example DSL for layered architecture:

foundation {
  layered {
    layer("application") {
      pattern(".*\\.application") {
        name shouldBe endWiths("DTO", "Request", "Response")
      }
    }
  }
  dependency { "application" dependedOn "domain" }
}

The DSL defines layers, naming conventions, and inter‑layer dependencies. The LLM fills in concrete elements, and a downstream processor validates the generated DSL.

Pattern: Explicit Retry for Language APIs

Intent: Increase deterministic output by retrying when the LLM produces an incorrect result while preserving conversation history.

Two retry strategies:

Resend the original prompt to obtain a fresh generation.

Send a corrective prompt that tells the model the previous output was wrong, keeping prior messages (higher token cost but more explicit).

Pattern: Dynamic Proxy Invocation

Intent: Dynamically map natural‑language inputs to external API calls or function signatures.

Typical mechanisms:

LangChain agents that select a tool based on a "Thought‑Action‑Observation" loop.

OpenAI function calling: the model returns a JSON matching a predefined function signature, e.g., send_email(to: string, body: string).

ArchGuard Co‑mate’s DynamicContext with DyFunction that supports reflective matching and model‑driven analysis.

Pattern: Local Function Dynamic Proxy

Intent: Use locally hosted NLP models (e.g., SentenceTransformer) to semantically analyze user input, match it to a function, and invoke the function locally; fall back to remote LLM processing when no match is found.

Summary

These five patterns—Natural‑language‑as‑DSL, Real‑time Streaming DSL, DSL‑guided generation, Explicit Retry, Dynamic Proxy, and Local Function Proxy—provide concrete architectural approaches for building language‑API‑centric applications, improving efficiency, accuracy, and user experience.