Python Developers: These AI Prompts Will Save You Hours
By My Code Diary
I wasted three hours last Tuesday writing boilerplate code I have written at least forty times before.
Not because I forgot how. Not because it was hard. But because I kept rephrasing the same vague prompt to ChatGPT and getting back the same generic, half-working answer. I knew what I wanted. I just did not know how to ask for it properly.
That gap, between knowing what you want and knowing how to ask for it, is exactly where most Python developers lose their time with AI tools. The model is not the problem. The prompt is.
After spending the last two years using AI assistants daily in production workflows, I have built up a small library of prompts that actually work. Not the generic “write me a function that does X” type. These are surgical. They cut straight to what you need.
Here they are.
1. The Boilerplate Killer
You know the drill. New project, same old structure. FastAPI setup, Pydantic models, env loading, logging config. It takes twenty minutes you will never get back.
The prompt:
You are a senior Python backend engineer. Generate a production-ready project scaffold for a FastAPI application that includes:
- Environment variable loading with pydantic-settings
- Structured logging with loguru
- A health check endpoint
- CORS middleware
- A sample router with one GET and one POST endpoint
Folder structure should follow domain-driven design. No placeholders. Working code only.
The key phrase here is “No placeholders. Working code only.” Without it, you get # TODO: add your logic here in every other line. That phrase has saved me more time than anything else in this list.
2. The Bug Explainer (Not Just the Bug Fixer)
Most developers prompt AI to fix bugs. That is useful. But what you actually want, especially when you are learning, is to understand why the bug existed in the first place.
The prompt:
Here is a Python function that is producing unexpected output:
[paste your function]
Expected output: [describe it]
Actual output: [describe it]
Do not just fix it. First, explain in plain English what is causing the bug and why Python behaves this way. Then show the corrected version with inline comments explaining each change.
This prompt turns a bug fix into a five-minute learning session. After a few weeks of using it consistently, you stop making the same class of mistakes.
Pro Tip: If you are getting a cryptic error you cannot parse, add “Explain this error like I have never seen it before, then show me how to reproduce it in isolation.” Reproducing in isolation is the real skill.
3. The Code Review That Does Not Pull Punches
Code reviews from AI tools are usually too polite. You get “this looks good, maybe consider adding a docstring.” What you need is the kind of review a senior engineer who has been burned by production failures gives you.
The prompt:
Review the following Python code as if you are a senior engineer preparing it for a high-traffic production environment. Be critical. Point out:
- Performance bottlenecks
- Hidden edge cases that could cause failures
- Security vulnerabilities if any
- Places where the code will break under scale
- Any Pythonic patterns being violated
Do not compliment anything. Only flag problems and suggest fixes.
[paste your code]
The line “Do not compliment anything” feels aggressive, but it works. It bypasses the AI’s tendency toward politeness and gets you straight to the issues.
4. The Automation Architect
This one is specifically for when you have a manual workflow you want to automate but you are not sure where to start. I used this when I was trying to automate a PDF reporting pipeline at a company I was consulting for.
The prompt:
I have the following manual workflow that I do repeatedly:
[describe the workflow step by step in plain English]
I want to automate this in Python. Break it down into:
1. The smallest logical steps that can each be a function
2. The best Python library for each step with a one-line reason why
3. Potential failure points and how to handle them gracefully
4. A rough code skeleton (function signatures and docstrings only, no implementation yet)
Notice you are asking for the skeleton before the implementation. This forces the AI to think architecturally instead of diving into code that looks right but is structured poorly. You review the skeleton, adjust it, then ask for the implementation.
# Example output skeleton you might get:
def fetch_raw_data(source_path: str) -> pd.DataFrame:
"""Load raw CSV from disk and return as DataFrame."""
pass
def clean_data(df: pd.DataFrame) -> pd.DataFrame:
"""Remove nulls, fix dtypes, deduplicate rows."""
pass
def generate_report(df: pd.DataFrame, output_path: str) -> None:
"""Render data as PDF report and save to output_path."""
pass
That skeleton is worth more than a hundred lines of AI-generated spaghetti.
5. The “Explain Like I Will Maintain This in Six Months” Prompt
Writing code is one thing. Writing code that you will still understand six months later, when you have forgotten everything about the project, is a different skill entirely.
The prompt:
Take the following Python code and rewrite it so that:
- A developer who has never seen this codebase can understand it within two minutes
- Every non-obvious line has a comment explaining the *why*, not just the *what*
- Variable names are descriptive and self-documenting
- Any complex logic is broken into smaller named functions
[paste your code]
The distinction between commenting the why and the what is critical. # increment counter is useless. # retry limit prevents infinite loops on flaky network connections is invaluable.
6. The Test Writer That Actually Tests Edge Cases
AI-generated tests are notorious for testing only the happy path. You ask for tests and you get three functions that all pass with normal input. The edge cases, the ones that actually catch bugs, are nowhere to be found.
The prompt:
Write pytest tests for the following Python function. Do not test the happy path first. Start with:
- Null and empty inputs
- Boundary values (min, max, off-by-one)
- Type mismatches
- Concurrent access if applicable
- Any input that could cause this to fail silently
Then write the happy path test last. Use parametrize where possible.
[paste your function]
Starting with failure cases first is a mindset shift borrowed from chaos engineering. It makes your test suite far more useful than the default AI output.
7. The Dependency Audit
Every Python project accumulates libraries. Some become unnecessary. Some have better alternatives. Most developers never audit them because it feels tedious. This prompt makes it fast.
The prompt:
Here is my requirements.txt:
[paste your file]
For each dependency:
1. Tell me what it is used for in a typical project
2. Flag any that are outdated or have known security issues as of your knowledge cutoff
3. Suggest a lighter or more modern alternative if one exists
4. Identify any that are likely redundant if another library on this list already covers that functionality
I ran this on a client project last year and found out they were importing both requests and httpx for different parts of the same codebase. Nobody had noticed.
8. The “Why Is This Slow” Prompt
Profiling code manually is genuinely painful. This prompt does not replace a proper profiler, but it catches the obvious sins fast.
The prompt:
The following Python code runs slower than expected. Without running it, identify:
- The lines most likely responsible for poor performance and why
- Any unnecessary iterations or redundant operations
- Data structure choices that are causing O(n^2) behavior or worse
- Opportunities to use vectorized operations, generators, or caching
Then show an optimized version with a comment on each change explaining the performance gain.
[paste your code]
The phrase “without running it” keeps the AI focused on static analysis rather than giving you a vague answer about profiling tools.
9. The Documentation Generator That Does Not Read Like a Robot
Auto-generated documentation is usually unreadable. This prompt changes the tone.
The prompt:
Generate documentation for the following Python module. Write it as if you are a developer who has used this code in production for a year and is explaining it to a smart new team member over coffee. Be specific, be direct, include at least one "gotcha" or non-obvious behavior, and skip any generic filler sentences.
[paste your module]
The “over coffee” framing sounds small but it completely changes the output. You get documentation that sounds like it was written by a human who actually cares, not a machine checking boxes.
What Ties All of This Together
The pattern across every prompt above is the same: specificity about what you do not want matters as much as specificity about what you do want.
“No placeholders.” “Do not compliment anything.” “Do not test the happy path first.” These constraints do more work than any positive instruction.
Good prompting is not about being clever. It is about being precise in the same way good code is precise. Every vague instruction you leave in a prompt is a decision you are letting the model make for you. Sometimes that is fine. But when you need production-quality output, you want to be in control of those decisions.
The developers getting the most out of AI tools right now are not the ones using the most advanced models. They are the ones who treat prompting as a skill worth practicing.
Start with one of these. Adapt it to your workflow. Break it, improve it, make it yours.
That is how you build the real skill.
