Speeding Up Development with AI and Cline

Over the past year at Designstripe, we've been extensively using AI tools, including Cursor, Copilot, and Cline, to help accelerate our development process. These tools have proven invaluable for prototyping and new feature development, particularly in the early stages of implementation.

Our experience shows that AI-generated code typically achieves 50%-75% feature completion before requiring developer intervention. While this sometimes significantly accelerates development, other instances require complete rewrites. Recently, we've focused on optimizing our approach to consistently achieve 75%-85% completion rates with minimal rework.

Common Challenges with LLM Code Generation

Through our experimentation, we identified several consistent challenges in AI-assisted development. First, the generated code often didn't align with our established coding patterns. For instance, while our codebase uses signals for state management in React, the LLMs would default to useState hooks. Second, we encountered "hallucinated" interfaces and functions - the LLMs would invent components that didn't exist in our codebase. Finally, we faced issues with over generation, where the tools would produce complete files with tests, requiring significant time to parse and modify.

A Structured Approach to Better Code Generation

Based on our learnings, we developed a three-step approach during a recent API integration project:

1. Detailed Analysis: Before engaging the LLM, we performed a thorough review of the existing codebase and created a detailed implementation plan for Cline to review and analyze all required code changes.

2. Context Setting: We provided Cline with a detailed prompt that includes our coding standards (such as using signals over useState), codebase structure, and information about our preferred libraries and patterns. For example, we specified our TypeScript configuration preferences and React component patterns.

3. Incremental Implementation: Instead of generating everything at once, we broke down the implementation into smaller steps, validating each change before proceeding. This included reviewing individual component updates, API integration code, and type definitions separately.

Results and Insights

This structured approach yielded mixed but promising results. The initial steps were remarkably successful, with generated code closely matching our standards and existing patterns. For instance, the LLM correctly implemented our signal-based state management and maintained consistent typing patterns.

However, we encountered limitations when implementing complex API integrations. The LLM began to lose context after the first three steps, reverting to generating complete files rather than maintaining our incremental approach.

Lessons Learned and Next Steps

Our experiment revealed several key insights. First, upfront planning and context-setting significantly improve code generation quality. The more specific we were about our requirements and standards, the better the results.

We also learned that smaller, more precise prompts tend to yield better results than attempting to generate large chunks of code at once. This approach helps maintain context and reduces the likelihood of hallucinated components.

Moving forward, we're planning to:

• Develop structured prompt templates that include codebase conventions, preferred patterns, and common pitfalls to avoid
• Break down complex features into smaller, focused prompts (limiting each prompt to a single component or function)
• Create a library of common coding patterns with examples from our codebase to improve context-setting
• Implement a validation checklist for each generated code segment

Conclusion

While we haven't yet achieved our target of 80-90% accuracy, these experiments have revealed that success with LLM code generation lies more in how we structure our interactions than in the capabilities of the tools themselves. By continuing to refine our approach and building on these learnings, we're steadily moving toward more efficient and reliable AI-assisted development processes.