VibeCoding Best Practices

Categories: Best practices, security, development, TDD, spec-driven, methodology, prompting

Created 11/16/2025 · Last updated 11/20/2025

Security and hygiene

Prioritize security from the start. Basic security hygiene is non-negotiable in vibe coding. Many first-timers have infamously hardcoded secret keys into front-end code or left databases open, leading to costly breaches. The best practice is to never expose API keys or credentials in code. Always use environment variables for secrets and ensure the AI-generated code accesses them securely on the server side.

Implement robust authentication on the backend. Do not rely on client-side logic that an AI might naively generate, as malicious users can easily bypass front-end checks. Move all security-critical checks to server code and use established libraries like Clerk or Better Auth rather than ad-hoc AI-generated login scripts. Additionally, validate all inputs rigorously to prevent SQL injection and XSS, as AI models often omit sanitization by default.

Leverage guardrails and audit tools. Platforms like Vercel’s v0 now integrate features that automatically scan deployments for insecure configurations. Using external code audit tools like CodeRabbit can also help catch vulnerabilities before shipping. The golden rule is to treat every AI-generated app as potentially insecure until verified.

Planning and specifications

Embrace spec-driven development. Handing the keys to an AI without a plan often results in a mess. Spec-driven development involves writing a structured plan for the AI to follow rather than prompting feature-by-feature. GitHub’s Spec Kit exemplifies this by using a spec file as a single source of truth.

Split development into phases. By organizing work into Specify, Plan, Task, and Implement phases, you impose discipline on the AI. First, prompt for a high-level design, then a technical plan, and finally implementation tasks. Reviewing and approving each stage prevents the model from hallucinating functionality or wandering off course.

Use templates for clarity. Providing the AI with a checklist regarding the app's purpose, data models, and constraints (such as performance or security requirements) ensures the generated code aligns with the vision. Upfront planning turns chaotic vibe coding into a predictable engineering process.

Verification and testing

Adopt test-driven development (TDD). AI models often produce code that works for the happy path but fails on edge cases. Prompting the AI to generate unit or integration tests alongside the code forces clarification of intended behavior. If tests fail, it serves as an immediate signal to refine the prompt or the code.

Utilize logging and monitoring. Explicitly instruct the AI to add logging statements around critical operations like database writes and API calls. Pragmatic debugging prompts—asking the AI to prioritize diagnosis over immediate fixes—often yield better results than random guessing.

Automate safety nets. Incorporate continuous integration (CI) early in the process. Tools like GitHub Actions can run AI-written tests on every generation, ensuring that rapid iteration does not break existing functionality. This creates a feedback loop that keeps technical debt low.

Prompt engineering

Craft clear, incremental prompts. Effective prompting breaks work into bite-sized tasks. Instead of asking for an entire app at once, start with a basic server, then add a specific route, then implement the logic. This step-by-step approach keeps the AI focused and reduces hallucination.

Be concrete and specific. Avoid vague instructions like "make it better." Instead, specify the libraries to use, the constraints to follow, and the exact success criteria. Providing context, such as pasting the current data schema or referencing a previous file, helps the AI maintain consistency across the project.

Iterate with feedback. When the AI errs, provide specific feedback on why the output was incorrect rather than restarting the prompt. Adopting a collaborative tone—treating the AI as a junior developer—and encouraging it to ask questions can significantly improve the quality of the output.

Tooling and environment

Use specialized tools. The ecosystem offers tools designed to enforce best practices. IDE plugins like Cursor and Copilot can explain code or suggest fixes, while AI code review tools like CodeRabbit can automatically flag logical errors and missed tests.

Deploy autonomous debugging. Advanced setups now allow AI agents to run the app, encounter errors, and suggest fixes autonomously. While these auto-fixes should always be reviewed, they dramatically reduce the manual effort required to stabilize a prototype.

Maintain human oversight. The most critical practice is to remember that the AI is a tool, not the architect. The human developer must remain in charge of high-level decisions, architecture, and final review. Interrogating the AI to explain its code helps maintain understanding and ensures the software remains maintainable long-term.

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