Artificial Intelligence (AI) may not yet be fully autonomous in developing entire products, but the rapidly advancing practice known as "vibe coding" is transforming software development through close collaboration between humans and AI. This innovative approach significantly accelerates how developers and product teams create software, enabling greater efficiency and productivity.

SashiDo’s team is excited by the potential of vibe coding, and we've prepared a detailed guide to help you maximize this emerging practice's potential - and have some fun in the process. In the next lines, we’ll share with you what vibe coding is and tips on how to successfully guide AI to adhere to the disciplined processes of professional software developers, so you can start building faster and smarter!

What Is Vibe Coding?

Vibe coding is a modern approach to software development where humans and AI collaborate in real time, using natural language prompts instead of traditional programming methods. Rather than manually writing every line of code, developers "vibe" with AI tools, guiding them through conversations, prompts, and corrections to generate, debug, and refactor code efficiently.

Photo by lhon karwan on Unsplash

At its core, vibe coding involves leveraging advanced AI tools like Claude Code, Windsurf, and Cursor to enhance traditional development workflows. It mirrors the early stages of prompt engineering - characterized by rapid innovation, continuous discovery, and evolving best practices. This new paradigm empowers developers to move faster, prototype more freely, and offload repetitive or complex tasks to AI.

More than just a technical shortcut, vibe coding is a mindset. It’s about embracing continuous experimentation and remaining open to new tools, methods, and workflows. Whether you're a seasoned developer or a curious newcomer, leaning into this approach allows you to harness AI’s full potential - build smarter and more creatively than ever before.

Tips and Tricks

1. Choose the Right Tools

In case you're new to coding or want quick visual results, tools like Replit or Lovable offer intuitive interfaces to prototype UI concepts easily. Many product managers and designers now go straight to implementation in code instead of mockups because it’s faster.

If you've coded before, even just a bit, you can directly try more advanced tools like Claude Code, Windsurf, and Cursor. Those are more robust, full-stack coding assistants that would allow you more precision and freedom while using them.

The next step would be turning your vibe-coded prototype into a scalable application, where using a fully managed BaaS like SashiDo can be a great choice. Our platform provides an improved Parse Open Source environment with real-time features, scalable infrastructure, and built-in tools that are especially handy for teams who want to skip the DevOps hassle and stay in the creative zone.

2. Defining the Scope and Developing a Clear Plan

Don’t start coding right away. Invest substantial upfront time interacting with pure language models (e.g., ChatGPT , Gemini, Claude, etc.) to define the project’s scope and architecture clearly. This prevents random or unhelpful code generation, ensuring AI-driven development remains aligned with your project goals.

For example, you can first work with your LLM of choice to write a comprehensive plan. Save it in a markdown file inside your project folder and refer to it often. Then follow these guidelines to make sure you stay on track with it:

Review your plan and delete or remove things you don’t like
Create a section with “parked ideas” for things you like, but won’t be implementing right away.
Work through your project one section at a time.
After implementing a section, run tests and commit the code.
Go back and mark that section as complete in the plan.

We expect models to improve, but for now, our suggestion is to work piece by piece rather than attempting to one-shot a full product, especially if it’s a complex one.

3. Use Version Control

A standard software development best practice is to adopt Git rigorously for version control. Don’t rely on AI tools’ internal revert features. Always work from a clean Git state when starting new features, frequently resetting to a stable state if AI-generated code introduces problems.

If you're trying multiple prompts to fix something, don’t let layers of bad code build up. Once you find a working solution:

git reset to clean up.
Feed that solution into the AI on a clean base.

This way, you can easily revert to a known working version in case the AI starts hallucinating.

4. Write Comprehensive, High-Level Tests

Another best practice from the standard development process is writing tests. In this case, you can ask your LLM to write tests for you - they're quite good at it. Focus on high-level integration tests rather than unit tests, and test everything end-to-end before moving to the next feature. These simulate user interactions and catch regressions early, especially when the LLM makes unnecessary logic changes.

5. Start with Test Cases

Start coding from clearly defined test cases you made manually. Handcraft comprehensive test scenarios to set strict rules for AI-generated code. When LLM passes these tests, your codebase's integrity is significantly improved without extensive micromanagement. Job is done!

6. Leverage LLMs for More Than Writing Code

Expand AI’s role beyond coding to include DevOps tasks such as DNS configuration and hosting setup. AI can also handle design elements like favicon creation and resizing, substantially accelerating project timelines. For example:

DevOps: Claude Sonnet can configure DNS servers and deploy them to Heroku.
Design: ChatGPT created a favicon; Claude can write a script to resize it into six other formats needed across different platforms.

Use them for everything from debugging to graphic generation.

Photo by Om Kamath on Unsplash

And if you want to go from idea to production quickly, SashiDo makes deployment and hosting frictionless. DNS setup, push notifications, and file storage are already taken care of, and our real-time logs and backups make monitoring easy. Learn more about setting up your app on SashiDo.

7. Simplify Bug Fixes with AI

Efficiently handle bugs by directly pasting error logs into your AI tool. Often, AI can immediately pinpoint and resolve issues based solely on error messages. For complex problems, systematically evaluate multiple potential fixes suggested by the AI, resetting often to maintain clean code.

When encountering a bug, here is the flow step by step:

Copy-paste the error message into the LLM.
Ask it to fix the issue - it often works with just a single prompt.
For complex bugs, ask the model to brainstorm possible causes first.
After each failed fix, git reset and start fresh.
Switch models if needed - Claude, GPT, Gemini all behave differently.

Again, make sure you follow good software development practices, use logging, and avoid stacking failed fix attempts.

8. Monitor AI Outputs to Avoid Infinite Loops

Regularly monitor AI-generated outputs for signs of repetitive or nonsensical code. If you get stuck where the AI IDE can’t implement or debug something and gets stuck in a loop, try going to the LLM's website, paste in your code, and ask the same question. Sometimes you’ll get a better result. Note that frequent manual copy-pasting of errors signals a deeper issue with the prompt or context. Step back and refine your inputs to clarify your intentions and ensure productive outputs.

9. Utilize Multiple AI Tools Simultaneously

Consider using multiple coding assistants simultaneously to enhance productivity. For instance, pair tools like Cursor and Windsurf on the same project, using Cursor for rapid UI adjustments and Windsurf takes a bit more time and is helpful for more thoughtful backend logic. Simultaneous use of tools can provide multiple iterations of the same feature, letting you choose the best outcome quickly.

Photo by Aerps.com on Unsplash

10. Use Instructions

Write instructions and put them in files, which you can name, for example, “cursor rules,” “windsurf rules,” or markdown guides. Each tool has different naming conventions, so keep that in mind. These make your AI coding agent more effective. Some users write hundreds of lines of instructions to achieve better results. It’s worth the time and effort if you’re serious about building something usable, and not just playing around. You can browse online for some ideas for what to put in those files, as there are quite many out there already.

11. Manage Documentation Effectively

Pointing LLMs to online documentation is still patchy. For consistent results, download essential documentation and then store it locally in a subdirectory within your project folders and instruct AI to reference these files directly before implementation. This method significantly improves the accuracy and context-awareness of AI-generated code.

You can also use LLMs as a teacher: ask it to walk you through the code it has just generated, line by line. It’s a great way to learn new technologies!

12. Modularize Your Codebase

Develop complex features separately as standalone projects or reference implementations. Integrate these isolated components using your AI tool.

Here is a short step-by-step flow for complex features:

Build a standalone, clean implementation first.
Or find and download a reference on GitHub.
Then ask the LLM to port that into your main codebase.

Adopting a modular, service-based architecture with clear APIs simplifies maintenance and scalability, and simplifies development for both humans and AI.

13. Select a Suitable Tech Stack

Choose frameworks with robust documentation and extensive conventions, preferably one you’re familiar with. AI performs extremely well when consistent, rich training data is available. Newer languages, like Rust or Elixir, for example, might offer less AI support initially but are likely to improve rapidly.

14. Embrace Vibe Coding as a New Programming Paradigm

View Vibe Coding as a new programming language where you're coding with natural language prompts. Provide detailed, context-rich prompts, as the quality of results heavily depends on the clarity and detail of your instructions.

15. Use Visual Inputs and Voice Interaction

Leverage visual aids like screenshots to effectively communicate UI issues or design inspirations to your AI assistant. Most agents support such type of input. Also, tools like Aqua enable voice-based interaction, significantly increasing input efficiency(you can dictate at 140 words per minute with the tool) and improving workflow dynamics.

16. Refactor Regularly

Once functional and tested, refactor your codebase frequently. Yet another golden best practice from software development that you just can't go wrong with. Request AI to identify redundant or unclear code sections for optimization. Regular refactoring keeps the project maintainable and transparent, facilitating smoother human-AI collaboration. Modular, short files make the whole codebase easier to understand.

17. Keep Experimenting

The AI landscape changes weekly, sometimes even daily. Right now:

Gemini: Great at whole codebase indexing and implementation planning.
Sonnet 3.7: Strong at code implementation.
GPT-4.1: Recently underwhelming, but improving.

Stay agile by continuously testing new AI models and methodologies. Different AI tools excel in various tasks like debugging, long-term planning, or feature implementation. Regular experimentation ensures optimal tool use tailored to evolving project needs.

Meet the SashiDo GPTs: Your AI Coding Sidekicks

To make vibe coding even more powerful on SashiDo, our team has created custom GPTs - AI assistants trained specifically to help you build faster on the SashiDo platform.

Whether you need to:

Write powerful Cloud Code functions
Debug Cloud Code instantly
Write and optimize complex queries using plain English
Craft mobile push notification templates and code snippets quickly
Import, export, and migrate your Database
Convert Voice to Parse Objects Instantly

Photo by Jacob Mindak on Unsplash

These GPTs are here to help, trained with deep knowledge of the SashiDo ecosystem and Parse Server.

Check out how SashiDo GPTs can boost your vibe coding workflow.

They’re like having a personal developer assistant by your side – 24/7, no coffee required.

Conclusion

Vibe coding represents a transformative leap in how we build software, blending human creativity with the power of AI to achieve faster, smarter, and more enjoyable development. By approaching AI as a capable collaborator rather than just a tool, and by grounding your workflow in proven engineering practices, you can unlock the full potential of this new paradigm.

As technology continues to evolve rapidly, the best way to stay ahead is to stay curious. Experiment, refine your process, and adapt your tools. Whether you're a seasoned developer or a curious beginner, vibe coding offers an exciting new frontier. Embrace it - not just to write code, but to reimagine what building software can be.

Ready to launch your vibe-coded app?

Whether you're prototyping with vibe coding or scaling a full product, the right infrastructure matters. At SashiDo, we’ve seen how pairing AI-assisted development with a robust backend platform empowers creators to bring ideas to life faster. If you're ready to launch your next project, start your Free Trial SashiDo and build without limits.