Introduction
Vibe coding turns clear natural-language prompts into working code. This practical playbook covers client selection, MCP usage, requirements, effective prompting, testing, and documentation. The goal is higher throughput with full developer control: humans own architecture, code quality, and decisions, reviewing every change before it ships.
Vibe coding fundamentals
Three parts drive the flow: the prompt (instructions and context), the client (e.g., Amazon Q Developer or Cline), and additional context (such as AWS MCP servers). AI accelerates work but does not replace judgment. Never trust generated code blindly: review it, validate dependencies, run security checks, and test in controlled environments.
Context
AI clients vary by IDE integration, pricing, policies, and MCP support (Tools, Resources, Prompts). A multi-client strategy can help: use Cline for backend/CDK and Q CLI for permissions, networking, and security group troubleshooting. Pick only MCP servers that match your use cases; read their docs and test them.
Choosing an AI client
Selection criteria
- Organization needs: compliance, security, approved vendors
- Technical factors: pricing, IDE integration, supported MCP features
- Compatibility: confirm Tools/Resources support if you rely on specific servers (e.g., CDK)
Leverage client features
- In Cline, start with Plan mode to align implementation; switch to Act mode after review
- Track docs and release notes to exploit new capabilities
Requirements and design
- Define scope and requirements upfront
- Set design and coding standards
- Document constraints and limitations
- Keep Markdown notes accessible to the client
- Begin coding only after these steps are complete
Use the assistant to explore requirements, architecture, and tasks, but remain the decision maker. Ask questions to invite critical feedback.
Effective prompting
- Provide detailed specs, inputs, expected outputs, and relevant files
- Apply prompts to focused tasks for easier review and testing
- Break large items into smaller subtasks
- Try metaprompting: discuss the feature, then summarize into an actionable prompt
- Consider prompt-optimization tooling for better inference results
Testing and validation
- Test incrementally after each change
- Automate with CI/CD and a maintained test suite
- Validate against original requirements
- Run automated security and quality scans
Unit tests written solely by AI are often superficial. Prefer TDD: you design the cases (including edge cases), and the assistant helps implement them. Ensure coverage and meaningful assertions.
Documentation
- Document every change (docstrings, README, project notes)
- Keep docs in sync with code
- Co-author with AI: ERD diagrams, APIs, networks—update docs and code together
Organized documentation preserves context throughout the project lifecycle.
Limitations and conversation management
Number of MCP servers and tools
- Too many integrations can slow down the client
- Follow client best practices and limits
Context management
- Long threads degrade performance
- Use separate conversations per feature
- Regularly prune history
Rules and configuration
- Define clear rules for code generation and edits
- Keep configs consistent across environments
- Document exceptions and review settings regularly
- Favor modular design
Rule examples
- Split files longer than 300 lines
- Add documentation for every new piece of code
Tooling and version control
Adopt best practices: modular code, static analysis, coverage, formatters, CI/CD, and tests. Coding agents can iterate by running tests and linters, self-correcting with minimal manual intervention.
Version control
- Frequent, descriptive commits (AI can draft messages; always review)
- Feature branches for new work and major changes
- Clean repo structure; define constraints early and include them in prompts
Conclusion
Vibe coding succeeds when rigorous engineering meets AI collaboration. With clear requirements, targeted prompts, strong testing, up-to-date docs, and a careful client/MCP setup, you ship faster without losing technical control.
FAQ
How do I capture requirements for vibe coding in a team setting?
Write scope, constraints, and standards in Markdown and expose them to the AI client before coding.
Which AI client fits vibe coding with MCP servers in 2025?
Pick based on policies, IDE integration, and Tools/Resources support. Test MCP servers aligned to your use cases.
How should I structure prompts for vibe coding on complex features?
Split into subtasks, attach files and context, and state expected outputs. Use metaprompting to distill plans.
Can I rely on the assistant to generate all unit tests in vibe coding?
No. Design the test cases yourself and use TDD; let the AI help implement them and keep coverage meaningful.
How many MCP servers are safe to enable without slowdown?
Only the necessary ones. Excess tools impact performance; follow the client’s documented limits.
What’s the best way to manage long vibe coding conversations?
Separate threads per feature, limit context size, and regularly clean history for stable performance.