How to Build Production-Ready Products with Vibe Coding

Vibe coding has moved well beyond the prototype stage. AI coding agents are now capable enough to help you build and launch a fully commercial product, even if you are not a professional developer. That is a remarkable shift, and it opens up genuine opportunities for founders, consultants, and small teams who want to build software without the traditional overhead.

But the ease of getting started can be deceptive. Generating code quickly is not the same as building something secure, maintainable, or cost-efficient. In this article, we cover the most important best practices for vibe coding: why they matter, and how to apply them from the very beginning of your project.

Why Do You Need to Follow Best Practices While Vibe Coding?

You want your product to be scalable and maintainable

AI-generated code can become messy very quickly. Without a clear architecture from the start, you may find yourself unable to add new features without breaking existing ones. Your product should always be in a working state, but AI can introduce regressions without realising it, especially across multiple sessions. Maintaining a consistent structure, writing tests, and reviewing changes at each step are all essential habits that keep your codebase healthy as it grows.

You want your product to be secure

AI assistants are optimised for producing working code quickly, but security is often an afterthought in their output. Common vulnerabilities include exposed API keys, missing input validation, and insufficient authentication checks. According to the GitGuardian State of Secrets Sprawl 2025 report, nearly 24 million hardcoded secrets were leaked on public GitHub repositories in 2024 alone, a 25% increase on the previous year. Notably, repositories using AI coding assistants showed a 40% higher secret leakage rate.

You want to optimise your costs

Token usage adds up faster than most people expect. A poorly structured codebase forces the AI to read more context on every request, burning tokens unnecessarily. Following good practices around documentation, context management, and prompt structure can make a real difference to your monthly bill.

The numbers are striking. TechCrunch reported in June 2026 that Uber burned through its entire 2026 AI coding budget by April, and Microsoft revoked Claude Code licences for its developers after individual engineers were spending between $500 and $2,000 a month on tokens. These are enterprise-scale figures, but the same dynamic applies at any level. The good news is that following the practices in this guide can significantly reduce unnecessary token consumption from the very start.

Guidelines for Vibe Coding Practices

1. Prepare Documentation for Your AI Agent

AI agents work best when they have clear, structured context about your product. Before you start building, prepare the following documentation and include it as context in your sessions:

• Product documentation: Write one general document describing what your product does, who it is for, and what problems it solves. Then add specific documents for critical flows such as the payment process, user registration, and any complex business logic.

• Design documentation: Define your primary and secondary colours, typography choices, and key design decisions such as dark mode support, accessibility requirements, and how the application should behave on mobile devices.

• Technical decisions: Briefly explain the libraries and frameworks you are using, where the application is hosted, and the overall architecture. Record important conventions such as always using type checking, always writing unit tests, and any other rules you want the agent to follow consistently.

2. Remind Your AI Assistant of the Current Date

AI assistants are trained on data with a cut-off date, which means they may be weeks or months behind on the latest framework versions, library changes, and industry trends. This can lead to outdated recommendations and subtle bugs when a model assumes an old API is still current.

Rather than asking:

"What is the best authentication solution for my application?"

Ask instead:

"What is the best authentication solution for my application as of June 2026?"

This simple habit ensures the agent reasons from the correct time frame and is more likely to suggest current, well-supported solutions.

3. Configure MCP Servers and Connectors

Model Context Protocol (MCP) servers allow your AI assistant to connect to external systems and fetch live, relevant information. This can dramatically improve the quality of the output and save you significant time.

• You can use the Vercel MCP server to debug deployment issues directly from your AI assistant.

• You can connect to Jira to pull in task details without leaving your coding environment.

• There are also MCP servers that give your assistant access to up-to-date documentation for specific libraries and frameworks, so it is working with current reference material rather than its training data alone.

4. Use Plan Mode Before Executing

AI assistants can make assumptions based on the quality of your prompt. Sometimes this saves time, but at other times it leads to work that heads in entirely the wrong direction.

Using a plan mode first, where the agent outlines what it intends to do before making any changes, allows you to catch misunderstandings early. You can correct the approach before a single line of code is written, rather than undoing work after the fact. Tools like Cursor and Claude Code both support this workflow natively.

5. Review the Work at Each Step

AI assistants have improved significantly, but they still make mistakes. Every change made and every command run should be checked before you move on.

A useful approach is to use plan mode and ask the agent to break the work into discrete steps, pausing for your confirmation at the end of each one. This gives you natural checkpoints to review what has changed, ask questions, and course-correct before problems compound.

6. Take Testing Seriously

Testing has always been important in software development, but it becomes even more critical when working with AI agents. There are a few reasons for this:

• AI assistants have limited context windows. They can forget earlier decisions they made and introduce regressions without realising it.

• Work done in one session is not automatically carried into the next. If you have not documented what was built, the agent will have no memory of it.

• The speed at which AI agents produce code means the volume of changes is much higher than in traditional development, making manual review alone insufficient.

Having a solid test suite and asking the agent to run the full set of tests at the end of each session is one of the most effective ways to catch issues early and maintain confidence in your codebase.

7. Follow Basic Security Practices

Security is one of the easiest things to overlook when you are moving quickly, and AI assistants will not flag it unless you ask them to. Before you ship anything, make sure your codebase does not contain hardcoded API keys, database credentials, or tokens. Use environment variables for all sensitive values and add a tool like Snyk or GitHub Advanced Security to your workflow to catch leaks early.

Authentication and authorisation are another common weak point in AI-generated code. Always verify that your application correctly restricts what each user can access, and never rely on the AI to enforce access control logic without reviewing it yourself. Row-level security, proper session handling, and input validation should be explicitly requested and then checked.

Finally, keep your dependencies up to date. AI assistants often suggest libraries that were current at their training cut-off, which may have known vulnerabilities by the time you are building. Running a regular audit with a tool like npm audit or pip-audit takes minutes and can surface issues that would otherwise go unnoticed until it is too late.

8. Have Your Work Reviewed

Even if you are an experienced developer, working solo on a vibe-coded project means there is no one to challenge your assumptions or catch what you might have missed. A second pair of eyes, whether from a colleague, a freelancer, or a technical advisor, can surface security gaps, architectural problems, and opportunities to reduce costs before they become serious issues.

This is especially valuable if you are not a full-stack developer with hands-on experience shipping and maintaining commercial products. AI agents are good at executing instructions, but they cannot tell you whether your overall approach is sound. An independent review of your code, your architecture, and your technical decisions can save you a significant amount of time and money further down the line.

Get Ready to Build Your Next Product with Vibe Coding

Vibe coding makes it possible to build real products with far less friction than before. But speed without structure leads to security vulnerabilities, rising token costs, and codebases that become difficult to maintain. The practices covered in this article, from preparing documentation and configuring MCP servers, to using plan mode, reviewing your work, and taking testing seriously, are the foundation of building something you can actually rely on.

Following these habits from day one will save you significant time and cost, and put you in a far stronger position when it comes to scaling your product.

At DoganTech, we offer a Vibe-to-Prod service as part of our Technology Consulting offering. From roadmap planning through to a scalable, production-ready product, we provide flexible, pay-as-you-go guidance. You continue to do the work yourself, but with the confidence that comes from having an experienced team review what you are building. We also offer structured Vibe-to-Prod assessments covering code quality, security, and architecture for teams who want a thorough independent review

Get in touch via our contact form to book your free 30-minute introduction call.