AI Workflows for Developers

AI Coding Workflow

When to use: Writing new code, implementing features, or exploring unfamiliar APIs and libraries.

Step-by-Step Process

• Describe what you're building with context and constraints
• Let AI generate initial implementation or boilerplate
• Review and refine the generated code for your specific needs
• Ask AI to explain unfamiliar patterns or libraries
• Iterate on edge cases and error handling

Tips for Better Results

• Provide context about your tech stack and conventions
• Ask for explanations of generated code you don't understand
• Use AI to generate tests alongside implementation
• Break complex features into smaller, focused requests

AI Debugging Workflow

When to use: Investigating bugs, errors, or unexpected behavior in your code.

• Paste the error message with relevant code context
• Ask AI to explain what the error means and potential causes
• Request debugging strategies, not just fixes
• Have AI help you write logging or reproduction steps
• Learn why the fix works, don't just copy-paste
• Include the full error stack trace, not just the message
• Mention recent changes that might have caused the issue
• Ask for multiple hypotheses when the cause isn't obvious
• Use AI to generate minimal reproduction cases

AI Documentation Workflow

When to use: Writing READMEs, API docs, code comments, or technical documentation.

• Provide the code or feature to document
• Ask AI to generate initial documentation draft
• Refine for your audience (developers, users, stakeholders)
• Add examples, edge cases, and troubleshooting sections
• Keep docs updated as code changes
• Specify the documentation format (README, API docs, tutorials)
• Include usage examples AI should document
• Ask for both quick-start and comprehensive versions
• Have AI generate documentation structure first

AI Refactoring Workflow

When to use: Improving code quality, reducing duplication, or modernizing legacy code.

• Share the code you want to refactor with context
• Explain the refactoring goals (performance, readability, patterns)
• Ask AI to propose refactoring options with trade-offs
• Review changes in small, testable increments
• Ensure tests pass after each refactoring step
• Have AI explain the benefits of each refactoring approach
• Ask for incremental refactoring steps, not big rewrites
• Use AI to generate tests before refactoring
• Request modern patterns relevant to your stack

AI Research Workflow

When to use: Exploring new technologies, comparing solutions, or learning new concepts.

• Frame your research question with context and constraints
• Ask AI to explain concepts and provide comparisons
• Request sources and references for verification
• Use follow-up questions to go deeper
• Summarize findings with actionable next steps
• Be specific about your use case, not just general questions
• Ask for pros/cons and trade-offs, not just features
• Verify important claims with official documentation
• Ask AI to challenge your assumptions

AI Testing Workflow

When to use: Writing unit tests, integration tests, or test strategies for your code.

• Provide the code that needs tests
• Ask AI to identify test scenarios and edge cases
• Generate test implementations for each scenario
• Review for coverage gaps and add missing cases
• Run tests and iterate on failures
• Request both happy path and error case tests
• Ask AI to explain what each test validates
• Include mocking strategies for external dependencies
• Use AI to generate property-based test cases

Build on These Patterns

These workflows are building blocks. Combine them to create your own AI-enhanced development process.