Introduction
Vibe coding is fun.
You open an AI tool, describe an idea, and minutes later, you have working code.
I built apps that way, too.
Some worked. Most didn’t last.
They were exciting experiments — but not reliable tools.
Over time, I realised something uncomfortable:
Vibe coding wasn’t enough.
If I wanted apps that I actually used — apps that saved time, automated workflows, and ran reliably — I needed structure.
A little vibe story
I built an AI-powered tool in one evening. It felt magical — until it broke when I needed it most. I couldn’t explain the architecture, trace the changes, or roll back safely. It worked, but it wasn’t built to last. I rebuilt it with a clear problem definition, a spec, milestones, and Git discipline. The second version didn’t just run — it held up. That’s when I realised vibe coding wasn’t enough.
AI can generate code in seconds — but without a structured AI coding workflow, it rarely produces reliable software.
From an Idea to a Ready App: My Vibe Coding Approach
I run a small tech blog on AI and Python. It grows fast, and I quite often use vibe coding to realise functionality that helps me run marketing or publication workflows.
For example, I used Cursor to convert a small Python script into a web application that sends my newsletter emails. The app saves me time and money, and I am not dependent on any third-party solution. The free subscription was enough to build this simple — yet time-saving — mailer in just a couple of hours.
I have some prior coding experience. I like coding in Python and running apps with Docker Compose. With basic HTML and CSS knowledge, you can build small web apps entirely yourself, but with Vibe coding, you can experiment and enjoy the scope creep as you go. 🙂
That said, scope creep was my biggest problem with vibe coding until I started working with AI coding assistance the way developers do — following a more structured approach that lets me deploy apps to requirements safely, in the shortest time, and with tight control over the process.
The Process That Leads to Useful Apps
Before I developed my vibe approach, my vibed apps often sat in my /git folder because I lost interest when I realised they weren’t helpful or actually what I wanted to achieve.
Now, I feel like a senior developer managing a team of three to six AI agents that together create the apps I want. Here are the tools I already use daily for the most boring — but necessary — tasks:
- Emails — A newsletter sending app I use at least once a week, completely free.
- AI Tools Finder — A tool to track the AI apps I write about and recommend.
- Images — A Python script that generates AI images under a permissive licence.
- Auto-Publishing — A Python script that converts my Markdown posts into Medium drafts in seconds.
- Website — Jekyll blog updates to add new functionality or update existing JavaScript.
- Subscriptions — A Python script to analyse and categorise my subscription expenses over a defined period.
And I could go on. I vibe code daily — here is what I have found, and what I recommend you try.
The Vibe-Coding Blueprint That Works
This approach works for me brilliantly. No orchestration workflows, no complex patterns. It is genuinely simple, and you do not need expensive AI tools or advanced coding skills — beginner proficiency is enough.
1. The App Idea and the Problem Statement
I usually get app ideas when I need to solve a specific problem. You might have a brilliant idea for a SaaS or website. Just ask yourself: will this app be more useful than existing solutions?
When vibe coding, I have also experienced scope creep and more features being added as we go, unless we define the problem just right at the beginning.
Add a small file PROBLEM_STATEMENT.md with clear answers to these questions in your mind:
- What does this product solve?
- Who is this for?
- What exact pain does it remove?
- What does success look like?
- What does NOT matter? Why?
Because specs drift if the problem is vague. This one small file prevents scope creep.
2. Choosing Your Stack
You can go with your preferred language and framework, or consult an AI for recommendations. For example:
“I want to develop a push notification system that sends my blog readers messages when I publish a new post. I prefer Python. What stack can provide the most efficient and secure implementation?”
You will get a solid starting point.
3. Creating Your Project Specification
Take the stack recommendation and continue with any AI tool to build a detailed project spec. Here is the prompt I use:
“I am creating a push notification service for my blog. Here are my main ideas: [paste your ideas]. Create a detailed implementation and deployment spec using the following stack: [paste the stack].”
Review the generated spec file and make sure that there are project constraints defined explicitly, for example:
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Performance constraints (e.g. <200ms response time)
-
Security requirements
-
Budget limits
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Hosting constraints
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Maintenance constraints
Example addition:
This must run on a $5 VPS, no managed services, no vendor lock-in.
That forces the architecture discipline.
Save the output to an AGENTS.md file, trim the unnecessary parts, and you have a solid spec to start vibe coding with.
4. Writing the Implementation Plan
Next, open your preferred AI coding assistant — Cursor, Codex CLI, or similar — and run:
“Develop a push notification system based on the project specification in AGENTS.md. Start by creating an implementation plan and saving it to IMPLEMENTATION_PLAN.md, and confirm with me before you start implementing. Make sure that each milestone includes unit tests.”
Please note that I have added the unit test requirement because AI is great at writing tests, and tests prevent silent regressions when you refine things later.
Codex CLI responded to me: “I’ve written the plan file and I’m quickly verifying its contents and structure before handing it to you for approval. Before I start implementation, tell me any corrections or additions you want.”
This is exactly what we want — a clear checkpoint before a single line of code is written.
5. Refining the Implementation Plan
Once IMPLEMENTATION_PLAN.md exists, I submit it to ChatGPT and ask for recommendations for improvement. Then I return to Codex CLI:
“Here are my additions: [paste recommendations].”
Adding Failure plan
Moreover, AI optimises for working code. It doesn’t automatically optimise for failure. So I added a Failure Plan section to my workflow. Before implementation, I ask the AI to identify risks, edge cases, and recovery strategies. The result isn’t more complexity — it’s more reliability.
You can use the following prompt to refine your IMPLEMENTATION_PLAN.md further:
Before implementation begins, update IMPLEMENTATION_PLAN.md by adding a new section titled "Failure & Risk Analysis".
Assume the product is deployed and actively used. Identify realistic failure scenarios based on its purpose, users, architecture, data flow, dependencies, and deployment model.
For each scenario, include:
- Root cause
- User impact
- Operational/business impact
- Detection method
- Recovery strategy
- Preventative measures
Focus on practical, production-level risks that affect reliability, trust, or usability — not theoretical edge cases.
Monitoring and Observability
After identifying failure risks, I add a “Monitoring & Observability” section to my IMPLEMENTATION_PLAN.md. It defines how the system reports its health, logs errors, and signals problems before users notice. Working software isn’t enough — I need to know when it stops working.
Monitoring is the process of tracking predefined signals to determine whether your system is healthy.
It answers:
Is everything working as expected?
Examples:
- Is the server running?
- Are error rates below 2%?
- Did the email job complete successfully?
- Is response time under 300ms?
Monitoring is about watching known indicators.
Observability is the ability to understand why something is not working when unexpected issues arise.
It answers:
Why is this failing?
Examples:
- Structured logs that show execution steps
- Error traces with stack context
- Request IDs to follow a user action
- Detailed runtime diagnostics
Observability gives you enough internal visibility to debug unknown problems.
Monitoring tells you something is wrong. Observability helps you understand why it’s wrong.
The minimal prompt example is as follows:
Add a "Monitoring & Observability" section to IMPLEMENTATION_PLAN.md.
Define how the deployed system will:
- Report health
- Log errors
- Track key metrics
- Trigger alerts
- Support debugging
Keep it lightweight and appropriate to the product’s scale.
6. Project Milestones, Tasks, and Version Control
You want your AI developer to be accountable and work in line with your plan. You also want to track all changes — I cannot recommend Git version control enough. It saves enormous headaches when you need to roll back.
Here is the prompt I use:
“I like the plan and approve it. Create PROJECT_COMPLETION.md with a table of project milestones and tasks in accordance with IMPLEMENTATION_PLAN.md. Update the checklist after each task. Initialise a git repository with an optimal .gitignore file and commit after each task completion.”
Codex CLI confirmed: “I’ll continue updating PROJECT_COMPLETION.md and committing after each completed task and milestone.”
7. Vibe Coding
Now you watch your AI dev work and update the PROJECT_COMPLETION.md checklist in real time. You will occasionally be asked for approval. You can ask questions, review decisions, and learn more about the code as you go. 🙂
8. Deployment and Running Instructions
Finally, the AI dev runs tests, wraps up PROJECT_COMPLETION.md, and reports on the project’s success. For deployment docs, just ask:
“Create a README.md with running and deployment instructions for [your hosting platform of choice]. Include the ‘Architecture Snapshot’ section with ASCII diagrams, including Component diagram, Data flows, Storage model”
In my case, Codex CLI actually created a DEPLOYMENT.md without me asking — a pleasant surprise.
Final Thoughts
This is not the ultimate pattern for every coding project, but it works for me every time. You can mix different agents depending on what you are building. I particularly like how Codex CLI handles the workflow.
A few tips before you start:
- Restrict your agent to a single directory and be careful about setting permissions too broadly.
- Start in approval mode, where the AI asks before acting — you will better understand how your project is built and which skills are involved.
To be honest, I am constantly adjusting my Vibe coding workflow, and I would add more details to it in the future to make it more bullet-proof and reliable, since AI likes to invent and requires more discipline :)
What do you think about this vibe coding blueprint? Is it different from what you do? I’d love to hear your approach in the comments.
