How AI Coding Transforms Complex Client Development: Methods, Challenges, and Efficiency Gains

As AI capabilities grow, using AI to generate code for client-side development has become an inevitable trend, yet adoption remains low due to the complexity of client business layers and limited AI familiarity among developers.

Core Principles

Practice is the only standard for truth. Our team started by enabling all client developers to use AI coding, then progressed to having AI complete actual business code, and finally explored AI‑friendly architectures and coding patterns.

AI Coding Issues Summary

Key problems include AI’s incomplete project knowledge, insufficient task descriptions, and context limits leading to hallucinations. Solutions involve enriching prompts with architecture details, providing sufficient context, and improving task decomposition.

Example: Claude Code can address sub‑agent architecture understanding, multi‑tool concurrency, and context compression to mitigate these issues.

Practical Workflow

We launched an AI coding practice where developers used oneDayNative to implement a daily requirement and reported problems. The workflow includes:

Analyzing existing services (e.g., InfoFlowService) and documenting dependencies.

Creating a multi‑info‑flow service ( MultiInfoFlowService) following microservice protocols.

Developing multi‑tab UI components (Template, Container, Component) within the TurboDressing framework.

Extending data‑binding protocols (e.g., adding isRepeat field).

Each step was measured for adoption rate, code quality, and efficiency. AI‑generated code required one person‑day versus six person‑days manually, achieving a 300% efficiency boost.

Prompt Engineering

Effective prompts dramatically affect output quality. Clear, detailed prompts with architecture and coding constraints yield correct results 20%–90% of the time. Prompt design guidelines include:

Specify exact output expectations.

Provide architecture diagrams and coding standards.

Break large tasks into smaller, high‑cohesion sub‑tasks to reduce context overflow.

Knowledge Base Construction

We built a knowledge base using project documentation (e.g., OneDayNative plugin, Cursor rules) and organized it into rule files covering architecture, component, template, service, page‑model, and UI‑tree structures. These rules guide AI in adhering to project conventions.

Development Paradigm

AI coding is most effective in well‑defined, architecture‑documented modules, especially during design and implementation phases where dependencies are clear. For bug‑fixes and cross‑module interactions, manual coding remains preferable.

Guidelines and Recommendations

Key takeaways:

Introduce AI early in architecture design only when documentation is complete.

Use AI for isolated UI logic or service modules to minimize coupling.

Maintain concise prompts to avoid hallucinations.

Continuously update knowledge base as project evolves.