GPT-Aided Iterative Python Development for Novices

GPT-Aided Iterative Python Development for Novices

1. Introduction

Learning to code is often romanticized as a moment of genius—someone sits down, has an idea, and builds it in one go. The reality is far less cinematic: it’s a loop of trying something, getting stuck, asking for help, and refining the result. For beginners especially, it’s the moment between getting stuck and finding help that can make or break the experience.

That’s where GPT comes in. By collaborating with a language model, a beginner can shorten the gap between confusion and clarity. Instead of searching through forums or tutorials hoping for a match, they can ask direct questions, get personalized answers, and keep momentum. It’s not about having all the answers—it’s about having a partner in the process.

This article walks through what it looks like to learn Python by building real, usable tools with GPT’s help. We’ll highlight practical examples, show how problems are resolved iteratively, and offer additional challenges you can try on your own. Whether you’re just starting out or revisiting Python after a break, these workflows are designed to build confidence through function, not perfection.

2. How to Collaborate with GPT

Working with GPT isn’t like typing code into a compiler—it’s more like having a conversation with a friendly, fast-thinking mentor. You don’t need to know the exact right question. In fact, it’s often more effective to think out loud and let the AI help shape your direction.

Here are a few helpful ways to get the most out of GPT as a coding partner:

Start with your intent, not the syntax

Don’t worry about writing a perfect prompt. Try something like, “I want to write a script that renames files in a folder.” GPT will fill in the blanks and help you start, even if you’re not sure what the final tool should look like yet.

Ask about errors directly

If you try code and it breaks, just paste the error message and say, “Can you help me fix this?” GPT can walk you through what’s wrong and offer suggestions or simplified versions that work.

Iterate like you would with a human

You don’t need to get everything right in one message. It’s completely normal to say, “Now can we add a way to save the output to a file?” or “Can we make this part clearer?” GPT learns from each step in the conversation and can build toward your goal with you.

Follow your curiosity

If something surprises you—or you just want to understand it better—ask! “Why did you use that library?” or “Is there a simpler way?” are great questions that can turn copy-paste moments into actual learning.

In the next section, we’ll walk through real examples of how this process unfolds—starting from an idea, stepping through challenges, and ending with working, usable Python scripts.

3. Full Walkthrough Examples

Now let’s see what it actually looks like to build something with GPT’s help. The examples below show a beginner-level Python utility growing from a basic idea into a working tool. Each one begins with a goal, runs into common obstacles, and evolves through conversation with GPT into something useful and complete.

These examples were chosen because they do three things:

  • They solve small but meaningful problems
  • They build on real-world needs (like saving links or cleaning up HTML)
  • They demonstrate how GPT helps along the way—not just by writing code, but by explaining and refining

Each walkthrough is structured to follow an iterative learning cycle: intention → first version → revision → final result → next steps. You’re encouraged to follow along, try variations, or reuse parts in your own scripts.

Example 1: Download and Parse HTML from a URL

Why This?

Sometimes you find a useful web page—a blog post, a news article, or a product page—and want to save part of it or reuse its content in another format. Instead of copying and pasting manually, we can use Python to download the HTML and extract specific information, like titles or links. This example introduces the basics of that process.

Initial Prompt to GPT

I want to create a Python script that downloads a web page and prints out all the links on it.

First Attempt

GPT suggests using two popular libraries:

  • requests – to download the page
  • BeautifulSoup – to make the HTML easier to work with

Here’s the script it returns:

import requests
from bs4 import BeautifulSoup

def main():
    url = input("Enter a URL: ")
    response = requests.get(url)

    soup = BeautifulSoup(response.text, 'html.parser')
    links = soup.find_all('a')

    for link in links:
        href = link.get('href')
        if href:
            print(href)

if __name__ == '__main__':
    main()

This script asks for a URL, downloads the page, and prints all the hyperlinks it finds. It works with most static HTML pages and shows useful output right away.

Explaining the Pieces

  • requests.get() fetches the HTML from the web.
  • BeautifulSoup() turns the raw HTML into something Python can search.
  • find_all('a') grabs every link tag on the page.
  • link.get('href') gets the actual URL behind each link.

If anything goes wrong (like no internet connection or an invalid page), GPT can help you add a try/except block later.

Improved Version with CLI Support

After prompting GPT again, we upgrade the script to take the URL as a command-line argument:

import requests
from bs4 import BeautifulSoup
import argparse

def main():
    parser = argparse.ArgumentParser(description="Download and list links from a web page.")
    parser.add_argument("url", help="The web page to fetch")
    args = parser.parse_args()

    response = requests.get(args.url)
    soup = BeautifulSoup(response.text, 'html.parser')
    links = soup.find_all('a')

    for link in links:
        href = link.get('href')
        if href:
            print(href)

if __name__ == '__main__':
    main()

Now the script runs like this from your terminal:

python get_links.py https://example.com

Result

This gives you a working tool that can extract hyperlinks from any public HTML page. It’s useful for saving references, checking sources, or building the next utility in your toolkit.

Extension Ideas

  • Filter links to only include those starting with https://
  • Save all links to a text file
  • Extract page titles, headers, or image sources
  • Ask GPT how to skip duplicate links
  • more advanced: ask about a new link detector from a saved history

Example 2: Simplify or Clean HTML

Why This?

When working with HTML—either from a saved file or a downloaded page—you’ll often find it cluttered with styles, classes, scripts, and deeply nested tags. If you’re converting HTML to Markdown, generating clean snippets, or preparing for further processing, it helps to simplify the structure. In this walkthrough, we use GPT to help us remove noise and reduce the HTML to its essential parts.

Initial Prompt to GPT

I have some HTML content. Can you help me remove things like <style>, <script>, and class attributes?

First Attempt

GPT suggests using BeautifulSoup again, but this time we’ll modify the HTML tree in place:

from bs4 import BeautifulSoup

def simplify_html(html):
    soup = BeautifulSoup(html, 'html.parser')

    # Remove all <script> and <style> tags
    for tag in soup(['script', 'style']):
        tag.decompose()

    # Remove class and style attributes from all tags
    for tag in soup.find_all(True):
        tag.attrs.pop('class', None)
        tag.attrs.pop('style', None)

    return str(soup)

This function removes unnecessary tags and strips styling information while preserving the text content and semantic structure. GPT may offer refinements like removing IDs, inline JavaScript handlers, or specific div wrappers.

Expanding to a Command-Line Tool

After asking GPT to turn this into a complete CLI script, it adds file I/O and argument parsing:

import argparse
from bs4 import BeautifulSoup

def simplify_html(html):
    soup = BeautifulSoup(html, 'html.parser')

    for tag in soup(['script', 'style']):
        tag.decompose()

    for tag in soup.find_all(True):
        tag.attrs.pop('class', None)
        tag.attrs.pop('style', None)
        tag.attrs.pop('id', None)

    return str(soup)

def main():
    parser = argparse.ArgumentParser(description="Simplify an HTML file by removing styles and scripts.")
    parser.add_argument("input", help="Path to the input HTML file")
    parser.add_argument("output", help="Path to the output simplified HTML file")
    args = parser.parse_args()

    with open(args.input, 'r', encoding='utf-8') as f:
        raw_html = f.read()

    simplified = simplify_html(raw_html)

    with open(args.output, 'w', encoding='utf-8') as f:
        f.write(simplified)

    print(f"Simplified HTML saved to {args.output}")

if __name__ == '__main__':
    main()

Result

This tool processes a local HTML file and strips it down to its essential content. It’s especially helpful for converting saved web pages into cleaner formats or prepping them for Markdown conversion. The result is easier to read, reuse, or reformat.

Extension Ideas

  • Preserve only structural tags like <p>, <h1>, <ul>, <li>
  • Remove nested divs that contain no text
  • Use GPT to help write custom filters (e.g., “keep only one image per article”)
  • Advanced: auto-generate a Markdown version from the simplified HTML

4. Practice Challenges – Try These with Your GPT

🧪 Practice Makes Practical

If you’re learning Python—or just trying to build something useful—working alongside a GPT can turn frustration into flow. Below are real-world utility ideas that are small enough to finish in an hour, yet valuable enough to use again and again. Each one is designed to be tackled collaboratively with an AI: you describe what you want, ask questions when stuck, and improve your solution as you go.

Try starting with the prompt we’ve suggested for each, then see where your creativity (and your GPT) can take you.

Each of the following Python mini-projects is designed to be solved iteratively with GPT’s assistance. They’re practical, beginner-friendly, and focus on real-world utility—ideal for learning how to collaborate with an AI while building useful tools. For each one, we’ve included a motivating scenario, a suggested prompt to begin with, and optional ideas for extending the project further.

📋 Get HTML from Clipboard as a CLI Utility

Why this?
You often copy content from a web page—tables, emails, or formatted text—and need the raw HTML for later use in scripts, templates, or processing. This utility extracts HTML from the clipboard and gives you control over how it’s handled.

Prompt to Start:

I want to create a Python command-line utility that reads HTML from my clipboard and prints or saves it.

Extension Ideas:

  • Add support for saving to a .html file by default.

  • Include a flag to output plain text instead of raw HTML.

✅ Command-Line Todo App Using JSON

Why this?
Sometimes you just want to jot down a quick task without switching apps or opening a browser. This utility lets you manage a simple todo list right from the terminal, storing tasks in a local file using JSON for easy readability and persistence.

Prompt to Start:

Help me build a command-line todo app in Python that adds, lists, and removes tasks saved in a file.

Extension Ideas:

  • Add timestamps or due dates

  • Support priorities (e.g., “urgent”, “later”)

  • Format output as a table or JSON block

  • More advanced: detect duplicates or automatically archive completed tasks

🗃️ List All Files in a Folder and Save to .txt

Why this?
You want a snapshot of a directory—useful for documentation, backups, or content tracking.

Prompt to Start:

I want a Python script that lists all files in a folder and writes them to a text file.

Extension Ideas:

  • Add file size and modification date.

  • Support recursive subfolder scanning.

📝 Convert Markdown to HTML

Why this?
You’ve written notes in Markdown and want to convert them into clean HTML for websites, reports, or personal archives.

Prompt to Start:

Can you help me write a Python script that converts a .md file into an .html file?

Extension Ideas:

  • Automatically name the output file.

  • Add a stylesheet link to the generated HTML.

📸 Rename Images with Timestamps

Why this?
Photos or screenshots often have generic names. This utility helps rename them using the date or a custom label—perfect for organizing by event, project, or timeline.

Prompt to Start:

I want a Python script that renames all .jpg files in a folder using the current date or a prefix.

Extension Ideas:

  • Include sequential numbering.

  • Add an option to rename only new files.

🧾 Simple Command-Line Todo App

Why this?
Need a quick way to jot down tasks without opening a browser or app? This script stores todos in a local JSON file and manages them via simple CLI commands.

Prompt to Start:

Help me build a command-line todo app that adds, lists, and removes tasks saved in a file.

Extension Ideas:

  • Add timestamps or due dates.

  • Support priority levels.

🔍 Search Text in .txt Files

Why this?
If you’ve archived logs, journal entries, or text exports, this tool helps you find phrases fast without opening each file.

Prompt to Start:

I want to search for a phrase inside all .txt files in a folder and print the matching lines.

Extension Ideas:

  • Highlight matches.

  • Add an option to save results to a summary file.

🕒 Command-Line Timer or Alarm

Why this?
Set a countdown when working, cooking, or focusing. No distractions—just a beep or message when the time’s up.

Prompt to Start:

Can we build a Python CLI timer that waits for a few minutes and then alerts me?

Extension Ideas:

  • Add a “label” to each timer.

  • Run multiple timers in background threads.

🌐 Check the Status of URLs in a File

Why this?
If you maintain lists of links (e.g., bookmarks, blog references), this script quickly reports which ones are broken.

Prompt to Start:

I want a script that reads URLs from a .txt file and checks their HTTP status.

Extension Ideas:

  • Save broken links to a separate file.

  • Add colorized CLI output for live vs. broken links.

✍️ Append Timestamped Notes to a File

Why this?
Need to take quick notes while coding or working? This script appends each note with the current timestamp—like a lightweight personal log.

Prompt to Start:

Can we make a script that takes text input and appends it to a file with a timestamp?

Extension Ideas:

  • Auto-create a new file each day.

  • Support Markdown formatting.

🧩 Wrapping Up the Challenges

These bite-sized utilities aren’t just stepping stones—they’re real tools you can use, adapt, and build upon. Whether you’re saving time, organizing data, or just experimenting, each one offers a chance to learn something meaningful with your GPT. And as you work through them, you’ll start to notice patterns—not just in the code, but in how you think, ask, and refine.

In the next section, we’ll explore a few best practices to help you get even more from your AI collaborator.

5. Best Practices for GPT-Paired Development

Once you’ve built a few utilities with GPT’s help, you’ll start to notice patterns—not just in the code, but in how you work. These practices can make your sessions smoother, your questions sharper, and your outcomes more reusable over time.

1. Work in small, clear steps

Break down what you want into manageable pieces. Instead of asking GPT for an entire script at once, try starting with a specific part: “Can you help me write the function that saves the file?” Smaller prompts lead to clearer answers and easier adjustments.

2. Version your ideas, not just your code

Asking GPT for variations can be powerful. Try saying things like, “Can we try a version that uses a class?” or “Let’s rewrite this to be more readable.” Treat each step as a draft—not a final answer.

3. Ask why, not just how

GPT doesn’t just write code—it can explain it. If something seems confusing or surprising, ask “Why did you choose that approach?” or “Is there a simpler way to do this?” These questions turn each session into a learning opportunity.

4. Keep a library of your working snippets

When a script works well, save it. Whether in a folder, a notes app, or a local HTML archive, keeping your working solutions makes it easier to reuse ideas later. You’ll start to recognize patterns—and maybe even write your own helpers.

5. Stay curious, not perfect

GPT doesn’t expect you to be an expert. Some of the most productive prompts start with “I’m not sure how to do this…” or “What would be a good way to…” Treat the process like guided exploration, not a test. That mindset builds skill naturally over time.

Even small tools—like the ones in this article—can teach powerful habits. And once you’ve built a few that work, the next idea gets easier. In the final section, we’ll offer a few closing thoughts and where to go from here.

6. Conclusion

You’ve now seen how GPT can act as a collaborator—not just a code generator, but a problem-solving partner that helps you explore, debug, and grow. If you’ve followed the walkthroughs or tried one of the practice challenges, you’ve built something real. And if you made a mistake and fixed it with a little help, that’s even better—that’s how real development works.

The tools you created might be small, but the process is the same one used for bigger projects: break the problem down, try a solution, ask for help when needed, and refine as you go. With GPT, those steps can happen faster and more interactively, especially when you’re still learning the language or the tools.

Whether you’re a beginner, a returning developer, or just someone with a few tasks to automate, the key takeaway is this: you don’t have to build alone. Your intent is the most important input. If you can describe what you want, GPT can help you shape it into something that works—and each iteration gets you closer to fluency.

So keep experimenting, keep asking, and keep building. The next script you write might save you time, teach you something new, or just make you smile. And the more you explore, the more powerful your tools—and your skills—become.