04. Architecture Design

Architecture design translates your product concept into a technical structure. Good architecture is not about predicting every future requirement—it is about making decisions explicit and ensuring components fit together coherently. This chapter walks through architecture decisions for local AI products.

Component Identification

Every product has three layers: input (how users interact with it), processing (what happens to input), and output (what users receive). Start by identifying these layers for your product.

For a local document search tool, the input layer includes a command-line interface and file selection. The processing layer includes document parsing, indexing, embedding generation, and similarity search. The output layer includes result formatting and display.

Data Flow Design

Map how data moves through your system. Start with the happy path: user provides input, system processes it, user receives output. Then identify edge cases and error conditions. Where can data be lost? Where can processing fail? What happens when hardware is insufficient?

# data_flow.yaml
data_flow:
  input:
    - user_query: string
    - source_documents: file[]
    - configuration: config
  
  processing:
    - parse_query()
    - generate_embeddings()
    - search_index()
    - rank_results()
  
  output:
    - formatted_results: string[]
    - confidence_scores: float[]
    - source_references: file[]

Technology Selection

Choose technologies that match your constraints. For local AI products, common choices include:

• Python for model integration and data processing due to ecosystem maturity
• Go or Rust for performance-critical components and CLI tools
• TypeScript/JavaScript for web interfaces andElectron-based desktop apps
• SQLite for local data storage (simple, no dependencies, portable)
• ONNX Runtime or llama.cpp for model inference without Python overhead

Justify each choice with concrete reasons tied to your project requirements. Avoid technology decisions made for theoretical future flexibility.

Directory Structure

Organize your codebase for maintainability. Separate concerns: models, data, configuration, and application logic should occupy distinct directories. Avoid deeply nested structures—three levels deep is usually sufficient.

my-first-ai-product/
├── src/
│   ├── models/          # AI model handling
│   ├── services/        # Business logic
│   ├── api/             # Interface endpoints
│   └── utils/           # Helper functions
├── data/                # Data storage
├── tests/               # Test suites
├── docs/                # Documentation
└── config/              # Configuration files

Architecture Documentation

Write an architecture document that explains your structure and decisions. Include a system diagram (even hand-drawn), component descriptions, data flow explanation, and rationale for key technology choices. This document helps future you understand why decisions were made and helps collaborators understand the system.