Vibe coding has received unwarranted criticism recently, with many dismissing it as a gimmicky approach to software creation. In reality, it's an effective method to accelerate development while maintaining good practices throughout the process.
Parallelism
Run 4 instances of Claude Code or Amazon Q chat simultaneously, assigning each to work on specific features based on a well-designed specification. Leverage AI to help enhance your specifications with detailed requirements. Remember that the quality of input directly determines the quality of output. Even advanced models like Claude 3.7 with Thinking mode have limitations - AI complements but doesn't replace original human thought.
Defined Feedback Loops through tests
After defining specifications, write tests upfront to codify how modularity is enforced. These unit tests establish the foundation of your defined user experience. Once you've set up the tests and configured how to invoke them, you can allow AI to complete the implementation. Well-defined feedback loops substantially increase your chances of success.
Important: Explicitly instruct AI systems not to modify tests and to focus solely on creating source code.
System Prompts and Guardrails
Develop a comprehensive contributing guide for your repository, including a README and guidelines on how software should be written for your team. This increases the likelihood that AI-generated code will stylistically match
your existing codebase. Enhance this with system prompts and clear guardrails specifying what practices to avoid.
Spot Bad Patterns
Incorporate a reflection step into your AI workflow by maintaining a troubleshooting guide in your repository. This helps the AI break out of problematic patterns or loops where it might otherwise get stuck.
Interrupt
Timely interruption is crucial. Treat your AI assistant as a collaborator you're brainstorming with, and provide immediate guidance when you notice it heading in an undesirable direction.
Security-First
Include security requirements in your initial specifications and prompt AI to highlight potential security concerns in its implementations. Have AI explicitly document security considerations for each feature it develops. Regularly scan AI-generated code with security tools and conduct threat modeling sessions to identify vulnerabilities that might have been introduced.
Version Control
If you are to let AI loose on making changes for features, remember to use version control and specifically commit changes that are for a given feature. This allows you "the orchestrator" to retain control and find exact cause of issues in the future. ala git bisect
Happy Orchestrating!