5 ChatGPT prompts I use every day as a Python developer

Rick — Tue, 12 May 2026 06:00:00 +0000

5 ChatGPT prompts I use every day as a Python developer
I've been writing Python for years — Flask apps on my own VPS, scrapers, trading bots, the occasional React frontend bolted on top. The last 12 months I've used ChatGPT (and Claude, and Gemini) daily as part of my workflow.
What I've noticed: most devs use AI like a search engine. They type "how do I do X in Python" and paste the first answer. That's fine, but it's leaving 80% of the value on the table.
The real leverage is in reusable prompt templates — structured prompts you fill in with your code or error, that consistently get you a useful answer. Below are five I use almost every day. Copy them, save them in a snippets file, thank me later.

The "explain this error like I'm tired" prompt When you've been debugging for two hours and the stacktrace is starting to look like ancient runes. I'm getting this Python error:

{paste full traceback here}

Here's the relevant code:

{paste 10-30 lines of context}

Explain in 3 parts:

What is actually breaking, in plain English
The most likely root cause given my code
The smallest possible fix

Don't rewrite my whole function. Don't add features. Just fix it.
Why it works: the "don't rewrite my whole function" line is critical. Without it, you get back 80 lines of "improved" code when you only needed one line changed.

The refactor prompt that doesn't go rogue For when a function works but you know it's ugly. Refactor this Python function for readability and maintainability.

Constraints:

Keep the exact same input/output behavior
Keep the same function signature
No new dependencies
Add type hints if missing
Add a one-line docstring if missing
Explain each change in a bullet list AFTER the code

Here's the function:

{paste function}
The "explain each change" part is what makes this useful. You learn why it's better, not just see different code.

The test generator that actually thinks about edge cases Generate pytest tests for this Python function.

Cover:

The happy path (1-2 tests)
Edge cases (empty input, None, wrong type, boundary values)
Any failure modes the code clearly handles

Use pytest fixtures only if genuinely needed. No mocks unless the function makes external calls. Group tests in a single class.

Function:

{paste function}
The trick here is being explicit about what you don't want (mocks, fixtures everywhere). Default AI output loves both.

The "is this code review-ready" prompt Before I open a PR I run this on the diff. Review this Python code as a senior developer would.

Focus on:

Bugs or logic errors
Security issues (especially around input handling, SQL, file paths)
Performance red flags
Naming and clarity

Don't comment on style if a linter would catch it. Don't suggest comments. Be direct — if it's fine, say "looks good" and move on.

Code:

{paste code}
The "if it's fine, say it's fine" instruction stops the model from inventing problems just to seem thorough. Game-changer.

The architecture sanity check For when you're about to commit to a design decision and want a second opinion before writing 500 lines. I'm building {short description of feature, 1-2 sentences}.

My current plan:
{bullet list of your approach}

Tech stack: {your stack}

Tell me:

What's the biggest risk in this approach
One simpler alternative I should consider
One thing I'm probably underestimating

Keep it to under 200 words total. I don't need a treatise.
This one saves me hours per week. Not because the AI is always right, but because forcing yourself to write down your plan in this format already surfaces half the issues.
A note on what makes these work
You'll notice a pattern: every prompt tells the model what not to do. That's not paranoia, that's the whole game. Default AI output is verbose, hedging, and tries to add features. Constraining the output is where the productivity gain lives.

I've collected 130+ of these — organized by use case (debugging, refactoring, testing, API design, deployment, SQL) — over at devpromptsbyrick.com . Launch-week discount is live until this weekend if you find this stuff useful.

Either way, save these five somewhere you'll find them. Future-you at 11pm debugging a production issue will thank you.

5 ChatGPT prompts I wish I had when I started learning Python

Rick — Tue, 05 May 2026 12:05:00 +0000

When I started learning Python a while back, I had ChatGPT open
in one tab and the docs in another. The thing is — I was using
ChatGPT wrong. I'd type "explain functions" and get a wall of
text I half-understood.

After a year of trial and error I figured out that how you
ask matters more than what you ask. Here are five prompts
that turned ChatGPT from a fancy search engine into something
that actually taught me Python.

These work just as well in Claude or Gemini.

1. The "explain like I just wrote it" prompt

Explain this Python code line by line as if you're
reviewing it with a beginner. For each line, tell me:

What it does
Why it's needed
What would happen if I removed it

Code:
[paste your code here]

Why it works: that third question is the magic. Forcing the AI
to explain what breaks if you remove a line teaches you the
purpose of every piece, not just the syntax.

2. The "find my bug without giving me the answer" prompt

I have a Python bug. Don't fix it for me.
Instead, ask me three questions that will help me
find the bug myself. Wait for my answers before
giving more hints.
Here's my code and the error:
[paste code and error]

This one changed how I learn. The instinct is to paste your
error and copy whatever fix the AI suggests. But you don't
learn anything that way. This prompt forces you to think
through the problem.

3. The "translate to Python" prompt

I want to do this in Python: [describe in plain English]
Show me three different ways to write it, from beginner
to advanced. Explain when I'd use each version in real code.

Useful when you know what you want to do but not how to
write it. Seeing three versions side by side teaches you
that there's rarely one "right" answer in Python — there
are tradeoffs.

4. The "quiz me" prompt

I just learned [topic, e.g. list comprehensions].
Test my understanding with 5 questions, from easy
to hard. Don't give me the answers yet. After I
respond, tell me which I got right and explain
the ones I missed.

I use this every time I finish a tutorial. Reading code is
passive. Getting tested forces you to actually retrieve what
you learned, which is when it sticks.

5. The "code review for beginners" prompt

Review my Python code as if I'm a junior developer
on your team. Be specific about:

What's good
What works but isn't idiomatic Python
What's actually wrong
One thing I should learn next based on this code

Code:
[paste your code]

This one is gold. Most code reviews online are either too
gentle ("looks great!") or too harsh ("this is terrible").
Asking for a specific format gets you the kind of feedback
a senior dev would actually give you.

A note on using AI to learn

The biggest mistake I made was treating ChatGPT as a
shortcut. It's not. It's a tutor that's available at 2am
when you're stuck. Ask it to explain, not to solve. Ask
it to test you. Ask it to make you uncomfortable.

The day you can write a prompt that teaches you something
you didn't know how to ask for — that's when AI starts
actually making you a better developer.

If these prompts were useful, I've packaged 50 of them
specifically for Python beginners over at
devpromptsbyrick.com.
Same format, organized by what you're learning —
fundamentals, debugging, projects, reading other
people's code.

What prompts do you use when learning Python? Drop them
in the comments — always looking for new ones.

DEV Community: Rick