Vibe Coding Quick-Start Guide: Best Practices & Tips - v1.2

Working with AI coding tools is like having that brilliant new hire who simultaneously knows everything and nothing. One minute they're writing perfect code, the next they're enthusiastically refactoring your payment system when you just asked them to change a font color. Don't believe me try vibe coding with very little planning. Without further delay here you go.

Vibe Coding Quick-Start Guide: Best Practices & Tips

Version 1.2

Best Practices for AI-Powered Coding

• Start with Detailed Specs: Outline a comprehensive requirements specification before coding. Include clear goals, database schemas, API endpoints, and desired architectures. A thorough spec guides the AI, reducing iterations and confusion. Tip: Use another AI (like Anthropic’s Grok or GPT) to help draft the spec. This spec becomes the blueprint you feed into Cursor/Windsurf for initial code generation.
• Set Clear Coding Guidelines: Define a consistent tech stack and coding patterns upfront. Provide the AI with a rules file (e.g., .cursorrules) that lists: Preferred frameworks, libraries, and tools (e.g., “Always use Node + Express with MongoDB,” or “Use NPM, not pnpm”). Style and design patterns (MVC structure, modular code, file naming conventions). Workflow constraints (separate dev/test/prod, commit frequency, etc.). These rules lock the AI to your tech stack and prevent unwanted deviations or tech switches.
• Iterate in Small Steps: Stay narrow with requests – address small tasks one at a time. For each coding change: describe the fix or feature, let the AI implement it, then test. This incremental approach minimizes compound errors and context overflows. Example: “Add a login endpoint” vs. “Build the entire auth system.” Small, focused prompts keep the AI on track and make debugging easier if something goes wrong.
• Prioritize Testing & Debugging: Treat AI-generated code with the same rigor as human code. After each change: Run end-to-end tests frequently to catch issues early. (User-style integration tests often reveal more than isolated unit tests). If tests fail, have the AI debug by prompting: “Explain why test X failed and suggest a fix.” Monitor that AI-proposed fixes address the root cause without introducing new bugs. Avoid placeholder data or mocks in production code – enforce using realistic data or well-defined stubs only in test environments to ensure reliability.
• Use Version Control & Checkpoints: Commit AI-generated code often. Frequent commits (with meaningful messages) let you roll back easily if the AI’s changes go astray. Maintain branch isolation for experiments – you can even run multiple Cursor windows on different branches in parallel. Save chat histories or transcripts of AI interactions. They act as a log of reasoning if you need to retrace steps or understand why code was written a certain way. Regularly refactor large files or complex code; the AI can assist in splitting code into manageable pieces, preventing monoliths.

Rules for AI-Driven Development

Establish explicit guidelines for the AI agent before coding begins. These rules act like a project’s constitution, ensuring every AI action aligns with your standards. Key rules include:

• Follow the Spec to the Letter: The AI should stick to the provided requirements. Don’t introduce features not requested. This keeps development on track and prevents scope creep.
• Prefer Simplicity: Always choose the simplest adequate solution. Avoid clever but complex code unless necessary. This reduces bugs and makes the code easier to maintain.
• No Unnecessary Changes: The AI must only modify related areas of the codebase. For example, if adding a new function, it shouldn’t refactor unrelated modules spontaneously. This rule prevents “AI tangents” that can introduce chaos.
• Avoid Code Duplication: Direct the AI to search the codebase for existing functionality before writing new code. If similar code exists, reuse or refactor it rather than duplicating logic.
• Enforce Environment Separation: Ensure the AI respects dev/test/prod boundaries. e.g., config files or constants must reflect the correct environment, and test code should never leak into production modules.
• Disallow Certain Practices: If you have banned practices (e.g., no mock data in live code, no external API calls in unit tests), state these clearly. The AI will then avoid those in its solutions.
• Specify Tech Stack & Architecture: List the frameworks, database, and architecture patterns to use. Also specify what not to use. (E.g., “Use SQLAlchemy for DB access; do not use raw SQL or an ORM I haven’t listed.”) This prevents the AI from using unwanted tech that might break your setup.
• Workflow & Style Preferences: Define how you want code delivered. e.g.: “Write self-documenting code with comments for complex logic,” “Include unit tests for new functions,” “Use a functional programming style where possible,” etc. By providing these up front, the AI’s output will be closer to your expectations in format and style.
• Enforce Configuration: Instruct the AI to parameterize the solution according to functional and environmental needs.
• Enforce a Changelog: Always use a changelog (e.g. located in the relative ./logs directory.)
• System Event Logging: Always add logging, with configurable logging parameters, and debug statements unless explicitly directed otherwise. Store logs in the relative ./logs directory in their own files (i.e. logging, debugging).

Prompt Engineering Guide (Crafting Effective AI Prompts)

• Be Specific & Descriptive: Vague prompts yield unpredictable results. Clearly state what you want and how. Include relevant details like function names, data formats, or user stories.
• Example: Instead of “Make this better,” say “Optimize the getUserData function for speed, possibly by reducing API calls or caching results.”
• Use Role & Task Instructions: Many AI coding tools let you set an assistant role or context. For instance, start with: “You are a Python backend expert following PEP8 standards”. Then give the task: “Add input validation to the user registration function following those standards.” This context primes the AI to respond with the appropriate expertise and style.
• **Leverage AI for Plans & Explanations: Don’t just have the AI write code. Ask it to outline a solution first.
• Planning Prompt: “Draft a step-by-step plan to implement feature X. Don’t write code yet – just outline the approach (data structures, functions, error handling).”
• Once you approve the plan, prompt: “Great, now implement step 1.” This chain-of-thought prompt style keeps the AI focused and lets you catch design issues early.
• Chain Prompts for Complex Tasks: Break down big asks into smaller prompt sequences. For a full-stack feature, you might:

1. Ask for a data model design (“Define DB tables and relationships for X”).
2. Then a backend API skeleton (“Create Express routes and controllers for these DB operations, no frontend yet”).
3. Next, a frontend integration (“Implement a React component to call the API and display results”).
4. Finally, tests (“Write integration tests for the new API endpoints using Jest”).
5. Each step’s prompt builds on the previous, reducing complexity and guiding the AI through the project structure.

Incorporate Examples & Constraints: If you have a format or example in mind, include it in the prompt.

• E.g. “Write a function to calculate X. For example, if input is Y, the output should be Z. Ensure the function handles null inputs by returning… (explain expected behavior).”
• Providing a mini-spec or example within the prompt clarifies the request and desired outcome.
• Use System/Rules Prompts if Available: Tools like Cursor allow special rule prompts or files to persistently instruct the AI. Use these for overarching guidelines like coding style, language level, or disallowed operations. (Think of it as a permanent prefix to every prompt.) It ensures consistency across all interactions, so you don’t repeat yourself every time.
• Voice & Natural Prompts: If using voice (Vibe Coder Extension), speak as if explaining to a colleague. Keep sentences clear and one instruction at a time. The AI is good at parsing natural language, so you don’t need fancy jargon – just clarity and completeness.

Common Pitfalls and How to Avoid Them

• Unclear Requirements → AI Misinterpretation: If your instructions are ambiguous, the AI might implement something incorrectly or make assumptions. Prevention: Always double-check that your prompt or spec is unambiguous. If the AI output seems off-track, clarify your requirements and try again. Sometimes a quick “Let me restate that more clearly…” fixes the issue.
• AI Going on Tangents (Scope Creep): You asked for a small change, but the AI modified extra files or added features you didn’t request. Why? The AI might be overzealous or trying to anticipate needs. Solution: Reinforce focus in your prompt (“Only change the Login() function and nothing else.”), and use the “Focus on Requested Changes” rule. If it still strays, undo those changes and guide it back: “Ignore the changes in other files, only do X.”
• Context Overload: These AI tools have context limits. If you prompt with too much code or discuss too many topics at once, the AI may lose track or forget earlier details. Avoidance: Work in small batches – limit each conversation to a cohesive task. Use multiple sessions or windows for different modules (e.g., one Cursor window focusing on frontend, another on backend). This way, each AI conversation stays concise and relevant.
• Tech Stack Drift: The AI might pick a tool or approach that’s not in your plan (e.g., switching your database choice or using a new library unexpectedly). Avoidance: That’s what the Rules file is for – explicitly ban or allow certain tech. If drift happens, gently correct the AI: “Our project uses Tech X for this, not Tech Y. Redo the solution using Tech X.” Also, run the spec by the AI again as a reminder of the correct stack.
• Overtrusting AI Output: It’s tempting to accept all AI-written code as correct, but bugs and logic errors happen. Mitigation: Always code review AI contributions. Use the AI to explain its code: “Walk me through how this function works, step by step.” This can surface logical errors or edge cases. Run tests to validate behavior (the AI can even generate those tests for you). Treat AI as a junior developer: helpful, but needing oversight.
• Lack of Testing: A common mistake is neglecting to test AI-generated code thoroughly, which can let bugs slip into production. Solution: Integrate testing into your vibe coding routine. After each major code generation, have the AI produce relevant tests or write your own. Ensure continuous integration is running tests on AI PRs. Basically, don’t deploy until you’ve verified the AI’s work in a safe environment.
• Getting Stuck or Confused AI: Sometimes the AI will get stuck in a loop or produce irrelevant answers (especially if the conversation got too long or complex). Fix: Reset the context or start a fresh session focusing on the problematic part. Provide a summary of what’s been done and where the issue lies, then ask for help on that specific point. Breaking the problem down further or rephrasing the question often helps the AI recover and provide useful output.
• Cost and Efficiency: If using paid AI APIs like Claude or others, “vibe mode” coding (free-flowing brainstorming) can rack up costs. It’s also less precise, which can waste time. Strategy: Use vibe coding for creative exploration or boilerplate generation, but switch to a more precise mode (or smaller model) for fine-tuning and repetitive tasks. Monitor token usage if cost is a concern, and set limits for each session. Sometimes, slower thoughtful AI (Claude 3.7 “Thinking” mode) gives better results than fast but shallow outputs, saving rework time.
• Over-reliance on AI: Remember that vibe coding is a partnership. If you find yourself blindly following AI suggestions that you don’t fully understand, pause. Note, while this is contrary to what others say on the topic, its best to understand the intent of the changes as opposed to becoming an automaton just accepting everything. I’ve tried it almost always results in rework. Remedy: Maintain an active role – read the code the AI writes, try to understand it, and ask questions. Use vibe coding to accelerate your work, not replace understanding. The best outcomes come when the developer guides the AI with insight, rather than just accepting whatever comes.

Here's my challenge to you: Take one small, non-critical feature in your backlog and try implementing it using these vibe coding best practices. Track the time it takes, the number of iterations required, and compare the result to your traditional development process.

Did you experience any of the pitfalls mentioned? Did the rules help keep your AI assistant on track? Share your experience in the comments – both victories and horror stories welcome. After all, we're all just trying to figure out how to code with these brilliant but sometimes chaotic digital colleagues.