Here’s something nobody talks about enough: the best AI-generated code almost never comes from the first prompt.

The first prompt gets you a solid starting point — maybe 70-80% of the way there. The real skill in vibe coding isn’t writing that first prompt. It’s what you do next. It’s the art of iteration.

Why Iteration Matters

When you’re coding by hand, iteration is natural. You write a function, run it, notice something off, fix it, run it again. You refine as you go. Nobody expects to write perfect code on the first attempt.

For some reason, people expect AI to nail it in one shot. When it doesn’t, they get frustrated: “See? AI-generated code isn’t good enough.”

But AI coding is the same as manual coding in this way — iteration is the process, not a failure of the process.

The difference is that each iteration cycle is much faster. Instead of rewriting code manually, you describe the change and get an updated version in seconds. A refactoring session that used to take an hour can happen in ten minutes across five or six iterations.

The Iteration Playbook

After months of refining my approach, I’ve settled on a pattern that works consistently.

Round 1: Get the Shape Right

Your first prompt should focus on the overall structure and the happy path. Don’t worry about edge cases, error handling, or optimization. Just get the basic shape of the solution right.

Write a function that parses a CSV file and groups the rows by the “department” column. Return a dictionary mapping department names to lists of rows.

Review the output. Does the overall approach make sense? Is it using the right data structures? If the foundation is wrong, iterate on that before adding complexity.

Round 2: Handle the Edges

Once the happy path works, add edge cases and error handling.

Update this function to handle: missing “department” column (raise ValueError with a clear message), empty CSV files (return an empty dict), rows with empty department values (group them under “Uncategorized”), and files with inconsistent column counts (skip malformed rows and log a warning).

This is where most of the quality lives. The difference between demo code and production code is almost entirely about handling the cases that shouldn’t happen but will.

Round 3: Refine the Interface

Now that it works correctly, make it pleasant to use.

Refactor this to accept either a file path or a file-like object. Add type hints. Rename the function from parse_csv to group_csv_by_column and make the grouping column configurable with a default of “department”.

This round is about the API surface — what will other developers (or future you) experience when they use this code.

Round 4: Performance and Polish

Only optimize after everything works correctly.

This function loads the entire CSV into memory. Refactor it to use a streaming approach that processes one row at a time, so it can handle large files without running out of memory.

Notice how each round builds on the last. You’re never trying to do everything at once. Each prompt is focused on one concern, and you verify it before moving on.

Conversation Techniques That Work

Reference Previous Output

You don’t need to repeat context. The AI remembers the conversation.

  • “In the function you just wrote…”
  • “The error handling you added — also apply that pattern to…”
  • “Keep everything the same, but change how…”

This is faster and less error-prone than re-describing the whole thing.

Be Specific About What to Change (and What Not To)

Change the return type from a dictionary to a list of NamedTuples. Don’t modify the parsing logic or error handling — just the output format.

Telling the AI what to preserve is just as important as telling it what to change. Without this, it might “helpfully” refactor parts that were already correct.

Ask for Alternatives

When you’re not sure about an approach, ask for options instead of a single solution.

Give me two different approaches for caching these API responses. One using in-memory caching and one using file-based caching. Show the tradeoffs of each.

This turns the AI into a design consultant, not just a code generator. You get to evaluate options and make an informed choice.

Use “What If” Questions

What happens if this function is called concurrently by multiple threads? Is there a race condition?

These questions help you think through scenarios without writing test code. The AI can trace through the logic and identify potential issues that might not be obvious from reading the code.

When to Stop Iterating

This is the hardest part. There’s always one more thing you could improve. Here’s my rule of thumb:

Stop when the code does what it needs to do, handles the edge cases that actually matter, and is clear enough that you’d understand it in six months.

Don’t iterate toward perfection. Iterate toward “good enough to ship.” You can always come back and improve it later — and by then, you’ll have real usage data to guide your improvements instead of guessing.

A Real Iteration Session

Let me show you how this plays out. I recently needed a function to sanitize user input for a search feature.

Prompt 1: “Write a function that sanitizes a search query string. Remove special characters, collapse whitespace, and trim the input.”

Got a basic function. Worked fine for normal input.

Prompt 2: “Also handle: null/undefined input (return empty string), extremely long input (truncate to 200 characters), and Unicode characters (keep them — users might search in other languages).”

Now it handled edge cases. But I realized I also needed SQL safety.

Prompt 3: “Add escaping for SQL LIKE pattern characters (%, _) since this will be used in a SQL LIKE query.”

Three prompts, three minutes, and I had a solid utility function that handles all the cases I care about. Each prompt was one clear concern.

The Mindset

The best vibe coders I know treat AI like a very fast collaborator, not an oracle. They expect to iterate. They plan for multiple rounds. They see each prompt as a step in a conversation, not a standalone request.

This mindset shift is the difference between “AI coding is frustrating” and “AI coding is incredibly fast.” The tool hasn’t changed — the approach has.

Next time you sit down to build something with AI, plan for at least three or four rounds of iteration. Use the first prompt to get the shape right, then refine in focused passes. You’ll be surprised how good the final result is — and how fast you got there.