Technical Design: Corporate Jargon Translator API Library (For Learning Purpose Only)

Today

Overview

in this document, I'll walk you through the development of the Jargonator library, a Go-based HTTP server that translates between plain English and exaggerated corporate jargon using OpenAI's API. While the functionality may seem straightforward, the design decisions and architecture show how to build a reusable, maintainable library that other developers can easily integrate into their apps

(disclaimer: this project is solely built for my http server learning purpose only)

Business Context

Corporate communication (at least from my experience) often suffers from:

Simultaneously, plain language can sometimes lack the authoritative tone expected in business settings.

Goal: Build a library that provides a humorous yet practical API for translating between plain English and corporate jargon, allowing developers to integrate this functionality into their own applications, chatbots, or tools.

Technical Architecture

I've adopted a modular, interface-driven design that follows Clean Architecture principles for several reasons:

Separation of Concerns

each component in the library has a single responsibility:

this clear separation makes the code easier to understand, test, and extend (hopefully)

Technical Decision: Interface-Based Design

Pros:

Cons:

I chose this approach because the benefits of testability and extensibility outweighed the costs of additional abstractions, especially given the need for mocking the OpenAI API during development and testing.

Folder Structure

jargonatorlib/
├── config.go           # Configuration struct and validation
├── jargonator.go       # Main library entry points (Server struct, Start/Shutdown)
├── handlers/
│   └── translate.go    # HTTP handlers for /to-jargon and /to-plain
├── translation/
│   ├── service.go      # Core translation logic
│   ├── prompter.go     # Prompt generation for OpenAI
│   ├── openai_api.go   # OpenAI API implementation
│   └── openai_client.go # OpenAI client with validation
└── examples/
    └── simple-server/   # Example server implementation

Code Overview

Configuration (Config)

type Config struct {
    Port                  string
    OpenAIAPIKey          string
    OpenAIModel           string
    RequestTimeoutSeconds int
}

Technical Decision: Configuration Struct with Validation

Pros:

Cons:

I chose this approach for its robustness and type safety, especially when handling critical configuration like API keys and timeouts.

Server Component

type Server struct {
    config     Config
    httpServer *http.Server
}
 
func NewServer(cfg Config) (*Server, error)
func (s *Server) Start() error
func (s *Server) Shutdown(ctx context.Context) error

Technical Decision: Using Standard http.Server

Pros:

Cons:

Translation Service

type Service struct {
    client   OpenAIClient
    prompter PrompterInterface
    model    string
}
 
func (s *Service) TranslateToJargon(text string) (string, error)
func (s *Service) TranslateToPlain(text string) (string, error)

Technical Decision: Specialized Prompter Component

Pros:

Cons:

The Prompter component was deemed essential because the quality of the translations depends heavily on well-crafted prompts, and this design allows prompt improvements without affecting the rest of the system.

OpenAI Client

type OpenAIClientImpl struct {
    api OpenAIAPI
}
 
func (c *OpenAIClientImpl) GetCompletion(prompt string, model string) (string, error)

Technical Decision: Official OpenAI Client vs. Custom Implementation

Pros of Official Client:

Cons of Official Client:

I chose the official OpenAI client for its reliability, maintenance, and proper handling of things.

Running the Project

Build

go build -o jargon-server ./examples/simple-server

Run Example Server

export OPENAI_API_KEY="your-openai-api-key"
./jargon-server

Test

go test ./...

Conclusion

The Jargonator lib demonstrates how even a simple functionality can benefit from thoughtful architecture. by following clean architecture principles, using interface-based design, and employing TDD, we've created a maintainable library that:

  1. is thoroughly tested at all levels
  2. Can be easily extended or modified
  3. Separates concerns for better maintainability

the project successfully fulfills its goal of providing a humorous yet practical tool for translating between plain English and corporate jargon, packaged as a library that other developers can easily integrate into their app.

view and clone the full project here: GitHub Repository — Jargonator

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