Vibe Coding Like A Pro

Let me tell you a secret. My latest web app — the one with the slick, responsive UI and a surprisingly robust backend — was built in a fraction of the time it should have taken. The secret wasn’t a new JavaScript framework or some revolutionary cloud service. The secret is that I didn’t write most of the code.

Instead, I had a conversation. Welcome to the era of Vibe Coding.

It’s a different way of thinking about building things. It’s less about knowing the intricate syntax of a `for` loop and more about translating your product vibe — that gut feeling for what looks good and works well — into reality through skillful prompting.

You’re not the code janitor in this new world, endlessly hunting for missing semicolons. You’re the creative director. The product visionary. And the LLM, like Google Gemini? It’s your brilliant, tireless, and occasionally quirky junior developer, ready to turn your ideas into actual, working code.

Your job is no longer to be the world’s fastest typist or to have every library method memorized. Your primary skill is shifting from writing code to managing complexity. It’s about being a world-class communicator who can clearly state your goals, guide the AI when it strays, and steer the project from a vague idea to a functional product.

This post is your field guide to making that shift. We will cover how to set up for success, have productive conversations with your AI partner, and turn the inevitable bugs into best practices. It’s time to learn how to direct.

Note: This post assumes you have little or no coding experience, but will touch on technical aspects that will help elevate your skills with using LLMs to create apps.

The Requirements Interview

You have an idea. Maybe it’s a full-blown app concept, or perhaps it’s just the next significant feature. Your first instinct might be to start thinking about code, UI design, or even technical assumptions about a library you heard about. I want you to resist that urge.

The most critical step in Vibe Coding happens before you think about implementation. It’s a simple, structured conversation I call the “Requirements Interview.” Its purpose is to take the vague idea from your head and turn it into a concrete plan, using the AI as your Socratic partner.

Start a new chat, not with a command, but with a role.

You: “Let’s plan a new app idea I have. I want you to act as an expert product manager for this conversation. Your job is to ask me questions to help clarify the requirements until we have a solid plan for a minimum viable product (MVP).”

Now, have a natural back-and-forth. Let the AI guide you through thinking about your idea.

AI: “Excellent. I’m ready. Could you give me the one-sentence elevator pitch for your app?”

You: “It’s a minimalist Pomodoro timer app designed to help users focus.”

AI: “Great concept. Let’s dig into the core features. What should the user be able to do with the timer? For example, can they set custom timer lengths for work and break periods?”

You: “Good question. Yes, they should be able to set custom lengths, but let’s have a smart default of 25 minutes for work and 5 minutes for breaks.”

AI: “Perfect. What happens when a timer completes? Should there be an audible alarm, a visual notification, or both? Should the next timer in the sequence start automatically?”

In just a few exchanges, you’ve already made key product decisions. You uncover edge cases and define the user experience before any code exists.

At the end of this conversation, ask the AI to summarize everything into a precise specification:

“Excellent. This has been a very productive conversation. I need you to synthesize everything we’ve discussed into a precise specification document for our project.

Please format your response using the following structure:

## Project Blueprint: [Your App Name Here]

### 1. High-Level Vision

A one-sentence summary of the app’s purpose.

### 2. Core User Stories (MVP)

A bulleted list of what a user can do. Please use the format: “As a [type of user], I can [perform an action] in order to [achieve a goal].”

### 3. Key Design Principles & Constraints

A bulleted list of the important rules, design vibes, and technical constraints we decided on (e.g., ‘Prioritize simplicity,’ ‘Must be mobile-first,’ ‘Notifications must have sound’).

### 4. Out of Scope for MVP

A bulleted list of features or ideas we explicitly decided not to build right now to keep the project focused.

Please be clear and concise. This document is the official blueprint we will use for the next steps.”

You won’t have code, but you’ll have something far more valuable: a blueprint. This blueprint is the raw material for the next crucial step: building your project’s Constitution.

Note: Some coding LLMs are so focused on coding that they don’t make good partners in the requirements conversation. You can use another LLM instead to pin down the requirements first. For my recent project, I used Google Gemini 2.5 Pro for the requirements and Claude Code for the development.

Crafting Your Foundational Prompt

To make that blueprint truly powerful, you must distill it into a reusable, tactical document. This is your project’s Constitution, the foundational system prompt guiding every coding session. It is where you step into the Director’s Chair and lay down the law.

Let’s walk through how to build your foundational prompt by pulling the most essential parts from your blueprint and adding a few universal rules.

Step 1: Start with The Best Practice

This one doesn’t come from your blueprint; it’s a universal best practice you should add to the very top of your prompt. It sets the fundamental tone of your collaboration with the AI.

“When in doubt, always ask the user for clarification before making a design or implementation choice.”

This single line prevents the AI from making wild assumptions when things get ambiguous, saving you from having to undo code later. The AI needs to ask follow-up questions when unsure what to build.

Step 2: Extract the Project Pitch and Core Principles

Now, look at the summary of your interview blueprint. It lists the most critical “vibe” and product decisions.

• Project Pitch: Find that one-sentence elevator pitch you defined.
  Example: “This is a minimalist Pomodoro timer app designed to help users focus. It should be clean, fast, and distraction-free.”
• Core Principles: Scan the conversation for key decisions you made about the user experience or design. These are your guiding principles.
  Example: “Users must be able to set custom timer lengths.”
  Example: “Prioritize a simple, clean interface over adding more features.”
  Example: “Notifications must include both an audible alarm and a visual cue.”

Step 3: Define Your “Do Not Touch” List

Your interview focused on what to build. This next part is where you, the director, add critical rules about what not to do. These are your non-negotiable guardrails.

Do Not Touch List:

• “NEVER commit API keys, secrets, or other credentials directly into the code. Use environment variables instead.”
• “NEVER modify the data folder without my explicit permission.”

Step 4: Add a Home for Future Lessons

Finally, a placeholder for the AI’s future learning should be created. This section will start empty, but trust me, it will become one of the most valuable parts of your document.

“Key Patterns & Lessons:

(This section will be updated automatically as we work.)”

And that’s it! You’ve created a powerful, reusable prompt by combining your blueprint’s vision with a few key directorial commands. Before you start any new coding task, you’ll use this entire text block as the starting context. It aligns your AI partner with your vision from the very first moment, every single time.

Note: Coding tools like Claude Code use the file CLAUDE.md in the project root as the system prompt for that project. Other tools or LLMs allow you to set an explicit system prompt or use the foundational prompt as the first prompt in your coding conversation.

From Vague Idea to Working Feature

Now comes the fun part: the actual conversation. This is where the magic happens, but things can go wrong if you’re not careful.

The biggest mistake people make is treating the LLM like a vending machine: put a command in, get code out. To get great results, you need to stop commanding and start collaborating. Think of it as a pair-programming session where you’re the senior navigator.

Here are the core techniques to master that collaborative flow.

Technique 1: One Chat, One Task

This is the most important habit you can build. Dedicate each chat session to a focused feature to keep your conversations clean and effective. Building a login page? That’s one chat. Fixing a bug in the navigation bar? That’s a new chat.

Why? LLMs have a limited memory (the “context window”). When you cram too many unrelated topics into one long conversation, the AI forgets earlier instructions, gets confused, and performs poorly. Starting fresh for each task keeps the context and objective clear, saving you money on tokens.

Note: This strategy is powerful because it relies on your foundational prompt. Think of the prompt as the “mission briefing” that gets the AI instantly up to speed for each new, focused task. The alternative is keeping one massive, ongoing chat. This might feel simpler, but it gets expensive fast and risks confusing the AI with old, irrelevant context. For a leaner and more precise workflow, we recommend focused chats. But know that you have options and can choose the best system for your use case.

Technique 2: When the AI Gets Stuck, Tell It to “Think Harder”

You’ll inevitably see it happen: the AI gets stuck in a loop, suggesting the same flawed solution repeatedly. This is your cue to intervene and force it to a higher level of thinking. Don’t just repeat your request; change the nature of the request.

Instead of asking for more code, ask for a plan.

“Pause. Before you write any more code, think step-by-step about how we should architect this new service. List the pros and cons of two approaches for me to review.”

Or force it to consider the boundaries of the problem.

“Let’s brainstorm the possible edge cases for this password reset flow. List at least five things that could go wrong for the user.”

Or help it find that tricky bug.

“Think hard about the issue and find a way to determine exactly where the cause is in the code. Come up with a strategy to fix this bug.”

These “meta-cognition” prompts break the AI out of its rut, often leading to a much more robust and well-thought-out solution.

Technique 3: When in Doubt, Print It Out

Sometimes, a feature doesn’t give you a nice, clean error message to copy and paste. It just… fails silently. The button does nothing, the data doesn’t save, or the wrong calculation appears. You’re flying blind because there’s no evidence of what went wrong under the hood.

This is when you need to ask the AI to turn on the lights. Instead of asking for a fix, ask for visibility.

“Okay, the user profile isn’t saving, and I’m not seeing any errors. Add detailed logging statements to show me the data at each step. Let’s get a play-by-play of what’s happening.”

This is the Vibe Coder’s equivalent of a detective dusting for fingerprints. It forces the invisible process into the open, giving you the concrete clues you need to guide the AI to the actual root cause of the problem.

Technique 4: Show, Don’t Just Tell

Your LLM is brilliant, but it’s working blind. It can’t see your screen, and it can’t see the error messages popping up in your browser (some IDE extensions might have access to these, but there are likely aspects still not visible to them directly). You are its eyes and ears. The more concrete evidence you can provide, the faster you’ll get to a fix.

• For backend bugs: Open your terminal or developer console, copy the entire error log (the whole “stack trace”), and paste it directly into the chat. For a web app, use F12 to open the console where logging messages are printed. Don’t summarize it; give the AI the raw data.
• For UI bugs: This is where multimodal models like Gemini shine. Take a screenshot. Mark it up in an image editor if you need to. Then, upload it with your prompt.
  “The button is misaligned on mobile; see screenshot-1.png. It should be centered and padded, like the button in this mockup screenshot-2.png.”

Showing is always better than telling.

Technique 5: The “Do It Like That” Pattern for Consistency

Keeping the UI and code style consistent as your application grows is a significant challenge. The easiest way to solve this is to stop describing what you want and start pointing to what you already have.

“I want you to build the new user profile page. It should use the same style, component structure, and state management pattern as the existing settings page. Use the code for the suggestions feature as your primary reference and model for the new page.”

This is the LLM equivalent of saying, “Do it like that.” It’s incredibly effective for quickly developing features while maintaining the integrity of your codebase.

The Debugging Loop

Let’s be real: no matter how good you or your AI partner are, you will have bugs. It’s a simple fact of building software. But in Vibe Coding, your role as the debugger changes completely. You’re no longer expected to be the expert who finds the broken line of code yourself.

Instead, you are the manager of the discovery process. Your job is to guide the AI in finding and fixing the bug. And most importantly, every bug becomes a free lesson that makes your AI partner smarter for the future.

Here’s how you do it. Once a bug is fixed, don’t just breathe a sigh of relief and move on. Initiate a reflective step.

The Learning Framework

This prompt forces the LLM to learn from its successes and mistakes. I recommend doing this every time you solve a non-trivial bug.

“Please update the system prompt and add comments to the code on what you learned about adding the feature. Add that ‘Most Crucial Advice’ to our foundational system prompt under the ‘Key Patterns & Lessons’ section. Think about what you learned. What is the most crucial advice you would give yourself regarding your strategy to add the feature?”

This step is the secret sauce. Remember that Key Patterns & Lessons section we added to our foundational prompt? This is how it gets filled. You are forcing the LLM to document its mistakes and create a permanent “memory” for your project. The next time you start a new chat for a new feature, that lesson will be part of the initial context, making the AI less likely to repeat the same error. It’s how your AI partner grows from a junior dev into a mid-level one.

Give Your Code a Memory

Your foundational prompt acts as the project’s long-term memory, but you can also give your code its own memory. Don’t just move on after you and the AI solve a tricky problem or implement a key feature. Instruct the AI to leave breadcrumbs for its future self in the code. We’re not talking about basic comments that explain what the code does; we’re talking about insightful comments that explain why it does it that way. This turns your codebase into a living document and a powerful source of context.

“This new function is perfect. Before we finish, please go back and add detailed comments. I don’t just want to know what it does; I want you to explain the reasoning behind our approach. Specifically, add a comment explaining why we decided to process these items in batches, mentioning that it’s to avoid the API’s rate limits. Think of it as a message to your future self so you don’t make a mistake here later.”

When you need to fix or update that file weeks from now, the AI will see its notes, instantly recall the original constraints, and be far less likely to re-introduce an old bug. It’s a powerful way to ensure that hard-won lessons stick.

Don’t Forget the Cleanup Crew

Debugging is often a messy process. To isolate a problem, ask the AI to add a bunch of console.log statements or disable a feature like caching. A common mistake is forgetting to clean up afterward.

Always remember the Restoration Principle. After the fix is confirmed, give one final command:

“Okay, that’s fixed, and the lesson is documented. Please remember to remove all the extra logging statements you added and re-enable any features you disabled for debugging. Give me the clean code.”

It’s a simple step, but it will save you from future headaches caused by leftover debugging code slowing down your app or cluttering your logs.

Always Check for Regressions

Here’s a habit that will save you from future headaches: after the AI adds or fixes anything, take a moment to test the old stuff. It’s common for a new piece of code to accidentally break an existing feature. This is called a “regression,” and it happens constantly. Before you move on, give your app a quick spin to ensure everything that used to work still does.

The Pro-Level Playbook

You’ve mastered the art of the conversation, and you’re turning bugs into brilliant lessons for your AI. You’re no longer just getting code; you’re building a system. Now it’s time to level up.

This is the pro-level playbook. It’s where you move beyond day-to-day features and start thinking like a lead developer, managing the bigger picture of cost, performance, and complexity.

Mastering the Token Budget

Let’s talk about the elephant in the room: using powerful AI models costs money. While it’s often a fraction of the cost of traditional development time, you still want to be smart about it. Being a pro Vibe Coder means being efficient.

The key is to use the right tool for the job. You wouldn’t use a sledgehammer to hang a picture frame, and you shouldn’t use your most powerful, expensive model for every task.

• Use the “Big Brain” Model for Big Problems: When brainstorming architecture, tackling a complex bug, or designing a new system from scratch, use the best model you have (like ChatGPT-4o, Claude Opus 4, or Gemini 2.5 Pro). Its reasoning capabilities are worth it.
• Use a “Workhorse” Model for Simple Tasks: Switch to a smaller, faster, and cheaper model for cranking out boilerplate code, writing documentation, making minor tweaks, or refactoring a simple function. It’s more than capable and will save your wallet.

Summarize, Don’t Scroll

As your conversation with the AI gets longer, two things happen: it gets more expensive (because you’re sending more context tokens with every message) and the AI can start to “lose the plot,” forgetting key decisions from earlier in the chat.

Instead of letting a chat session sprawl into an endless scroll, you need to be the one who calls for a “context refresh.” When you feel a task is halfway done or the conversation drifts, ask the AI to create its summary.

“Okay, this chat is getting long. Let’s compact our context. Please summarize the key decisions and code we’ve written for this feature so far into a single block. Include the main goal, the libraries we chose, and the current state of the main component’s code. This summary will be our new starting point.”

Then, you can start a new chat with just that summary to continue the work with a clean slate.

Some specialized tools are starting to streamline this. For example, Claude Code has a command /compact that can automate this process. But knowing how to do it manually with a prompt gives you the power to manage your context effectively with any AI, on any platform.

The Universal Performance Strategy

When your app starts to feel sluggish, it can be tempting to tell the AI, “Make it faster!” But that’s too vague. Instead, you can direct a professional performance review using a simple, three-step mantra: Measure, Identify, Defer.

1. Measure First: You can’t fix what you can’t see. Start by asking the AI to help you get data.
   “Help me add some basic performance monitoring to this feature. I want to know exactly how long it takes to execute when a user clicks the button.”
2. Identify the Cause: You can form a hypothesis once you have numbers. Often, lag isn’t one big slow thing, but a thousand tiny fast things happening simultaneously.
   “The logs show many small updates. Is the cumulative effect of all of these firing at once what’s causing the lag on the dashboard?”
3. Defer, Don’t Dither: The key to a snappy user experience is to do the critical work now and delay everything else.
   “Let’s create a strategy to defer any non-critical operations (like updating the side panel) until the user has finished typing. The typing itself must feel instantaneous.”

This high-level approach works for almost any performance problem and doesn’t require you to be a systems engineer.

Validate, Don’t Trust: Challenging the AI’s “No”

This might be the most crucial advanced technique of all. LLMs can be confidently wrong. They will sometimes state with absolute certainty that a library doesn’t support a feature or that something is impossible when it is, in fact, very possible.

A beginner accepts the AI’s “no.” A pro becomes a skeptical partner.

If Claude tells you something can’t be done, your next move should be to spend two minutes on Google Gemini or checking the official documentation for the tool in question. More often than not, you’ll find that it is possible, and the AI just needs a little nudge. Copying and pasting a link to the relevant docs is often all it takes to break the deadlock.

“The new version of that library does support this. See the documentation here: [link]. Please read this and then try to implement the feature again.”

Learning to challenge your AI partner respectfully is how you’ll push past its limitations and solve the challenging problems.

Your Vibe Coder’s Cheat Sheet

That was a lot, I know. We’ve gone from big-picture mindset shifts to nitty-gritty debugging tactics. So, let’s distill it all down.

If you’re going to take anything away from this post, make it these core habits. This is your quick-reference card for being an effective Vibe Coder. Print it out, stick it on your monitor, or keep it in mind before opening your next chat.

Conclusion

Vibe Coding isn’t about some magic button that replaces developers. It’s about a fundamental shift in your role. You’re learning how to pair program with a tireless, fearless, and swift junior developer who just happens to speak perfect code.

Its job is to handle the syntax, the boilerplate, and the heavy lifting.

Your job is to hold the vision. You’re the one who provides the taste, sets the direction, asks the hard questions, and catches the mistakes when the AI gets a little too enthusiastic. You are the senior partner in this collaboration. The quality of the final product doesn’t depend on the AI’s skills alone, but on the quality of your guidance.

The clearer your vibes, the cleaner your shipped code.

The techniques in this guide are your starting point, but the real skill is building your intuition. You’ll learn which prompts work best for you and how to “read” your AI partner’s responses. You’ll understand when to push, clarify, and start a fresh chat.

This is a new and compelling way to build things. Now, create something amazing.

Check out my reading list of other Google Gemini articles.

This post was created with the help of AI writing tools, carefully reviewed, and polished by the human author.