Effective Vibe Coding - A Practical Guide

After several months of daily AI-assisted development across multiple projects, from building VulcanAI to wrestling with LIDAR data processing in robotics, I’ve developed a workflow that consistently produces high-quality results while avoiding the common pitfalls that plague many AI coding initiatives. This post distills those lessons into a practical framework for effective vibe coding, building on the insights from my previous exploration of AI-assisted development.

Disclaimer:

In this article, I am presenting my opinions and practices based on my personal experiencs with Cursor, Github Copilot, and other AI coding tools. There is no objective “right way,” and what matters is finding a methodology that works for you and meets your needs.

I am also oversimplify and personifying AI agents quite a bit. It is important to understand how these agents actually work to understand their strengths and weaknesses, but that is beyond the scope of this article. I recommend my recent post about AI agents as a starting point for more information.

What AI Coding Agents Are and Aren’t

Before diving into techniques, it’s crucial to understand what we’re actually working with. AI coding agents are neither the silver bullet that will eliminate the need for skilled developers nor the useless gimmick that some critics claim.

What They’re NOT

AI agents are not a replacement for skilled human developers. They lack the experience, architectural vision, and problem-solving intuition that comes from years of building real systems. They can’t make the high-level decisions that determine whether a project succeeds or fails. Some businesses, lured by the promise of rapid cost savings, have made the mistake of firing experienced developers and expecting non-programmers to use AI tools to generate code with minimal oversight. This approach is deeply misguided and inevitably leads to disaster. Without the judgment and expertise of skilled developers, projects quickly accumulate technical debt, suffer from poor architecture, and become difficult to maintain or scale. AI-generated code, when left unchecked, often misses critical edge cases, security concerns, and long-term maintainability considerations that experienced developers instinctively account for.

Similarly, AI agents are not a fire-and-forget “easy button” for development. The fantasy of describing an application in a single sentence and getting production-ready code is exactly that—a fantasy. AI agents require careful guidance, constant oversight, and frequent course correction. They work best when treated as sophisticated tools that augment human capability rather than replace human judgment. As I discussed in my cautionary follow-up on vibe coding, blind trust in AI-generated code can lead to serious technical debt and maintenance nightmares.

What They ARE

What AI coding agents actually represent is souped-up auto-complete—the natural evolution beyond tab-complete and Microsoft IntelliSense. They understand context at a much deeper level and can generate substantial blocks of coherent code, but they’re fundamentally an extension of the same concept rather than something entirely revolutionary. This perspective is important because it sets appropriate expectations about their capabilities and limitations.

More importantly, AI agents serve as a force multiplier for human developers. Their real value lies in eliminating the tedious, repetitive work that consumes so much developer time: boilerplate code, basic documentation, simple CRUD operations, test scaffolding, and other routine tasks that every developer has written hundreds of times. By handling these mundane but necessary tasks, AI agents provide liberation from the routine, freeing human developers to focus on what actually matters—system architecture, complex problem-solving, performance optimization, and the interesting technical challenges that require genuine creativity and insight.

General Usage of AI Coding Tools

The key to successful AI-assisted development is adopting the right mindset and approach. After working with AI agents across multiple projects, I’ve found that treating the interaction like project management rather than traditional programming yields far better results.

Think Like a Project Manager

Classical software project management techniques work exceptionally well with AI agents, and this isn’t coincidence. These methodologies were developed to coordinate work between humans with varying skill levels and perspectives, which maps surprisingly well to human-AI collaboration. Instead of writing tickets for team members, you’re writing detailed prompts for your AI agent, but the same principles apply: be specific, include acceptance criteria, and provide necessary context. Roadmaps and schedules become context that helps AI agents understand the bigger picture and make better decisions about implementation details and architectural choices. Test Driven Development and Pair Programming prove extremely effective because the AI can run tests autonomously and iterate on solutions until they pass, creating an immediate and objective feedback loop.

Start with Design Documentation and Be Specific About Everything

Before writing a single line of code, establish a clear plan. A design document written in Markdown and stored in your project repository provides essential context for every AI interaction. At the start of each coding session, remind the agent to review this document as your north star. AI agents excel at drafting robust design documents from simple descriptions—give them a rough outline of your project requirements and let them flesh out a comprehensive design document, then review and refine it by adding the nuance and project-specific details that only you understand.

When interacting with AI agents, describe what’s needed without any ambiguity. Specify language, platform, coding standards, libraries to use or avoid—anything that matters should be stated directly. AI agents can’t read your mind or infer your preferences from incomplete information, but they excel when given comprehensive context. Include relevant documentation: style guides, library documentation, API references, even hardware manuals if applicable. AI agents can identify and apply relevant information from a broad library of context, but they need access to that information first.

Treat It Like a Talented Intern

This analogy has served me well across multiple projects and captures the essential dynamic perfectly. Your AI agent has more raw technical knowledge than you do—it’s familiar with more languages, frameworks, and APIs than any human could be—but it completely lacks your experience, domain knowledge, and understanding of the bigger picture. Like a bright intern, it can implement sophisticated solutions but needs guidance on what to implement and why.

Give the agent autonomy to implement solutions, but review every line of code it generates. Test Driven Development proves particularly valuable here because it provides an objective measure of correctness that the AI can verify independently, creating a feedback loop that often catches issues before you even see them. However, do not trust the AI blindly. AI agents can be sneaky in subtle ways—they’ll sometimes “fix” a failing test by modifying the test rather than the code, or they’ll solve the immediate problem while creating architectural debt elsewhere. This behavior isn’t malicious, but it reflects the AI’s focus on immediate objectives rather than long-term consequences.

Always insist on thorough documentation. Inline comments and comprehensive documentation serve dual purposes: they help you understand and verify the code, and they help the AI remember what the code was supposed to do in future interactions. Undocumented code becomes a liability for both human and AI developers, leading to confusion and inconsistent behavior over time. When the AI uses a method or technique you don’t recognize, ask it to explain. Often you’ll learn something valuable about modern development practices, but sometimes the AI will realize during the explanation that its approach contains a bug and fix it immediately.

Finally, periodically ask the AI to update the project README or write architectural summaries. This serves multiple purposes: it gives you insight into the AI’s current understanding of the project structure, provides valuable documentation for future developers (human or AI), and creates rich context for future AI interactions with the codebase.

My Usual Workflow

Through trial and error across multiple projects, I’ve settled on a five-phase workflow that consistently produces good results while maintaining architectural coherence and code quality.

Context Preparation forms the foundation of every project. Before writing any code, I populate the AI’s context with all relevant resources: style guides and coding standards for the chosen language and framework, documentation for libraries and APIs I plan to use, example code from similar projects when relevant, and any domain-specific documentation or requirements. This upfront investment in context preparation pays dividends throughout the entire development process. It is important to understand that context serves as the long-term memory of the AI agent, and most agents have some means of attaching documents and links to the context to persist between interactions. Furthermore, documentation and comments in the code itself can serve as memory by moving information out of the short term context and into the long term context.

README-Driven Development begins each project with clarity of purpose. I start by writing a basic README explaining what the program should do, then ask the AI to expand this into a comprehensive README for an in-development project. This expanded version includes detailed feature descriptions, an implementation plan with milestones, technology stack justification, and an architecture overview. I carefully review and edit this document because it becomes the foundation for all subsequent development—both the AI and I refer back to it constantly to maintain focus and consistency.

Skeleton-First Architecture diverges from traditional incremental development. Rather than building features one by one, I instruct the AI to create the complete program structure upfront using placeholder code for unimplemented functionality. This approach ensures a coherent architecture from the start and identifies potential integration issues early, before they’re expensive to fix. The skeleton serves as a roadmap that guides all subsequent implementation work.

Test-Driven Implementation provides objective success criteria for every feature. I describe test cases in detail and have the AI set up the testing framework and implement the tests I’ve specified. This creates clear success criteria and enables autonomous verification of implementations, allowing the AI to iterate on solutions independently until they pass all tests. This approach mirrors the methodical testing strategies I apply in hardware projects like building the Mite robot, where incremental validation prevents costly integration failures.

Iterative Feature Development forms the core development cycle: implement tests for the next feature, implement the feature to pass those tests, validate against the full test suite, clean up documentation and comments, then repeat. I take periodic breaks during this cycle to review the overall architecture, update documentation, and ensure the AI hasn’t drifted from the original design goals. This rhythm maintains both momentum and quality throughout the project lifecycle.

Conclusion

Effective vibe coding isn’t about finding ways to avoid thinking about code—it’s about directing that thinking toward higher-level concerns while delegating routine implementation tasks. The developers who thrive in this new paradigm will be those who can effectively orchestrate AI agents while maintaining the architectural vision and quality standards that separate good software from garbage.

The technology is still evolving rapidly, but the fundamental principles of good software engineering remain constant. AI agents are powerful tools, but like any tool, their effectiveness depends entirely on the skill and judgment of the person wielding them.