How to Start Vibe Coding: A First-Session Walkthrough

This post is a practical vibe coding tutorial for someone who has already decided to try it and wants a concrete first session. If you are still deciding whether it is worth trying, start with the primer on what vibe coding is and where it works. What follows does not cover production work, anything with real user data, or the general argument for why you should adopt the practice. Think of it as a first-session walkthrough, nothing more.

What you need before you start

You need three things: a model with a chat interface, an environment to run code, and enough programming background to test what the model produces.

On the model: a current chat model with code support is sufficient. You do not need a paid subscription to start a first experiment. If you want to practice without committing to a plan, there are free LLM APIs to try first.

On the environment: a terminal and a text editor is sufficient. Purpose-built IDEs with model integrations make the iteration loop faster once you have done it a few times, but they are not required for a first session.

On programming background: this is the part most guides skip. You do not need to be able to write the code yourself. You do need to be able to run it, read an error message, and judge whether the output is correct. If you cannot tell a right answer from a wrong one, you will not know when to stop iterating. You will cycle until the code looks plausible without knowing whether it is right.

A PM who has done some scripting, a junior developer, a founder who can read a stack trace: you are in the right place. Someone who has never read an error message will hit a wall at the first failure, because they will have no handle on what to type back into the chat.

Choose your first project

The best first project is something disposable. That is not a compromise; it is the correct choice.

Disposable means: no one is waiting on it, it does not touch real user data, and you will not have to maintain it or explain it to anyone next week. The ideal scope is something you would otherwise write by hand in an afternoon, or a small interface that exists to test an idea rather than to ship one.

Projects that work well:

• A script that reads a folder of files and prints the name, size in kilobytes, and last-modified date for each one
• A single-page form that checks whether an input is a valid email address and shows a message
• A data transformation that reads a JSON file and reshapes it to match a different schema

Projects to avoid for a first attempt:

• A feature in an existing codebase you are actively shipping (the generated code will not match your conventions, and the mismatch is hard to catch)
• Anything that handles authentication, payments, or stored user data
• A project you intend to deploy and maintain

If you are not sure, pick the smallest thing on the list. A second project is easy to start once you have done one.

The prompt-run-fix loop

This is the core skill you are building. Write a prompt describing what you want, accept the generated code, run it, and respond to what you see. Here is what one cycle looks like across a ten-minute example.

Step 1: Write a clear first prompt

Describe the output you want, not the method. For the file-listing script: "Write a Python script that reads all files in the current directory and prints the file name, size in kilobytes, and last-modified date for each one, formatted as a table." Include the language, the inputs, and the shape of the output. Specificity on the output reduces how much you have to correct in later cycles.

Step 2: Paste the generated code and run it

Do not edit the code first. Paste it into your editor, run it, and look at what comes out. Is the table showing what you expected? Are the numbers plausible?

Step 3: Describe what you see, not what you think caused it

If the output is wrong, do not try to diagnose the code. Describe the symptom: "The modified dates are all showing today's date instead of the actual modification date of each file." Paste that back into the chat and run the new version.

Step 4: Check a boundary case

Does the script still run if the directory is empty? Does it handle a filename with a space? Try one or two of these before declaring the work done. You are not looking for edge-case perfection; you are checking that the normal-case logic does not break on obvious inputs.

Step 5: Decide whether it is good enough

For a disposable script, good enough means it produces the correct output on the input you have. You do not need to audit the code or make it readable. Run it, check the output, stop when the output is correct.

That is the loop. On a task this size it takes ten to twenty minutes. After two or three cycles it starts to feel mechanical, and you develop a sense for which prompts get useful output on the first try.

Common failure modes

The code runs without error but produces the wrong answer. This is the hardest failure to catch because there is no stack trace. The defense: before you trust the script on real input, run it against a case where you already know the correct answer.

The iteration stalls. You have pasted the error back three times and the model keeps generating code with the same problem. This usually means the prompt is not giving the model enough context. Add more: what the input data looks like, what the expected output is, what you already tried.

The scope grows. You finish the script and realize it would be better with one more feature, then another. This is how a disposable project becomes a maintained one. The moment you find yourself building something to keep, stop and make a deliberate choice: is this still a first experiment, or has it become a project that needs real attention?

The model is confidently wrong. Models produce plausible-sounding errors without flagging uncertainty. On a first session, check any output that matters against something you can verify independently.

When to stop vibing and read the code

The line is not about code quality. It is about consequence.

Read the code when: someone else has to use the output, the script handles data you cannot recover if it is corrupted, the code is going anywhere near a production environment, or you cannot explain what it does when something breaks.

Reading it does not mean rewriting it. Run through it once, confirm you can follow the logic, and know what to look at if it fails later. That takes ten minutes on a short script. The difference between reading and not reading is the difference between owning the code and hoping it keeps working.

This is where learning to vibe code stops being about a first session and becomes a broader question: what do you actually need to understand to work this way sustainably? The primer has the full argument; this post is just the first session.

Next steps

The primer at /blog/what-is-vibe-coding covers the full context: what vibe coding is, how it compares to traditional and agentic coding, and the specific situations where its failure modes become serious. If you found this post first, that is the right next read before you scale up.