ChatGPT Prompts for Python Development

Why isn't this function working?

def calculate_discount(price, discount_percent):
    return price - (price * discount_percent)

The Problem the Developer Faced

Picture this: you're a junior developer with a function that passes every test case except one, and you've been staring at it for forty minutes without seeing why. This is one of the most common situations chatgpt python prompts get used for, and it's also where the difference between a vague prompt and a well-constructed one shows up most clearly.

The Wrong Approach

The developer's first attempt pasted the function alone, with no test case or error detail:

Why isn't this function working? def calculate_discount(price, discount_percent): return price - (price * discount_percent)

What this does: without the failing test case or expected output, the model can only guess at what "not working" means -- it might flag a real issue (discount_percent probably needs to be divided by 100 if passed as a whole number like 20 instead of 0.2) or it might focus on something irrelevant, since there's no signal about which specific behavior is wrong.

⚠️ Common mistake: Asking "why isn't this working" without including the specific failing input, the actual output you got, and the output you expected instead. This forces the model to guess at the bug's nature rather than diagnose it directly from the evidence.

The Correct Prompt

The fixed version includes the specific failing case and both outputs:

This function fails for one test case. Function: def calculate_discount(price, discount_percent): return price - (price * discount_percent) Test case: calculate_discount(100, 20) should return 80 (20% off $100), but it returns -1900. All other test cases with discount_percent as a decimal (0.2) work correctly.

What this does: providing the specific failing input, expected output, actual output, and the detail that decimal inputs work fine gives the model everything needed to immediately identify the actual bug -- discount_percent is being passed as a whole number (20) in the failing case instead of a decimal (0.2), so the function needs either input validation or a division by 100.

⚡ Pro tip: Always include the specific failing input, the actual output you got, and the output you expected, especially when only one case out of many fails. The contrast between the working cases and the failing one is often the fastest path to the actual root cause.

Results and What Changed

With the added context, the actual bug (unit mismatch between whole-number and decimal percentage inputs) surfaced immediately instead of requiring another round of back-and-forth guessing. The junior developer noted that once she started including the failing case and expected-vs-actual output as a habit, her average time to a working fix dropped noticeably across unrelated bugs too, not just this one.

A senior engineer at the same company applies a similar principle to a more subtle class of bug: intermittent failures that don't reproduce consistently, which are notoriously harder to debug from a code snippet alone.

This function fails intermittently, maybe 1 in 20 calls, with no clear pattern I've found. Function: [paste]. It processes items from a queue and writes results to a shared dictionary. I suspect this might be a race condition since it's called from multiple threads, but I'm not certain.

What this does: sharing the hypothesis (race condition, shared dictionary, multiple threads) alongside the intermittent symptom directs the model's attention to the specific class of bug most likely responsible, rather than starting from a blank slate on a bug that's inherently hard to diagnose from static code alone.

How to Apply This to Your Situation

For code review rather than debugging, a team lead uses ChatGPT to get a second opinion on a colleague's pull request, providing the same kind of context -- not just the code, but what it's supposed to do and any specific concern:

Review this function for potential issues. It's meant to validate user-submitted email addresses before saving to our database. My specific concern: I'm not sure the regex handles international domain names correctly. Function: [paste]

What this does: naming the specific concern (international domain handling) up front focuses the review on the area most likely to have a real issue, rather than a generic "any problems?" review that might spend equal attention on trivial style issues and the one thing that actually matters for this function's correctness.

⚠️ Common mistake: Requesting a generic code review without flagging your own specific area of uncertainty. A reviewer -- human or AI -- who knows what you're actually worried about can focus their attention there, rather than distributing equal scrutiny across the whole function including parts you're already confident about.

A data scientist refactoring a script originally written as a quick one-off analysis uses ChatGPT to make it production-ready, with a clear scope for what "production-ready" means in this context:

Refactor this analysis script to be production-ready: add error handling for missing or malformed input files, add logging instead of print statements, and add type hints. Don't change the core analysis logic itself -- I've already validated that it's correct.

What this does: scoping the refactor explicitly to structural improvements while protecting the validated core logic from unintended changes prevents the common failure where a "cleanup" pass accidentally introduces a subtle logic change alongside the intended structural improvements.

Learning and Explanation Requests

Beyond debugging and review, developers use ChatGPT to understand unfamiliar code, whether inherited from a previous team member or pulled from an open-source library. A new hire onboarding onto a legacy codebase uses a specific technique for this that goes beyond a generic "explain this code" request:

Explain this function, but specifically: walk through what happens with a concrete example input, showing the value of each variable at each step, rather than a general description of what the function does.

What this does: requesting a step-by-step trace with a concrete example, rather than an abstract description, produces an explanation that's much easier to verify against your own understanding by mentally following along with actual values, instead of accepting a plausible-sounding but harder-to-verify general summary.

⚡ Pro tip: When trying to understand unfamiliar code, always ask for a walkthrough with a concrete example input rather than a general explanation. Concrete traces are far easier to verify against your own understanding, and they surface edge cases a general description tends to gloss over.

A developer learning a new library uses ChatGPT to bridge from a pattern she already knows in a different library, which tends to produce a much faster learning curve than generic documentation:

I know how to do pagination with the requests library in Python using manual offset parameters. I'm now using a library called httpx for async requests. Explain how pagination typically works with httpx by directly comparing it to the manual approach I already know.

What this does: anchoring the explanation to a pattern already understood, rather than starting from zero, produces a much faster and more concrete learning path than a generic "how does httpx work" question, since the comparison highlights exactly what's different rather than requiring the reader to build a full new mental model from scratch.

Handling Ambiguous Requirements

Sometimes the challenge isn't a bug or unfamiliar code, but a genuinely ambiguous feature request that needs clarifying before any code gets written at all. A contractor working on a client project uses ChatGPT to surface the ambiguities in a vague requirement before committing to an implementation approach:

A client asked for a function that "validates user input" for a signup form. Before I write this, list the specific validation rules this could reasonably mean (email format, password strength, required fields, etc.) so I can confirm with the client which ones they actually need rather than guessing.

What this does: using ChatGPT to enumerate the range of reasonable interpretations, rather than picking one interpretation and building it, surfaces the ambiguity for a client conversation before time gets spent building the wrong thing -- a much cheaper place to catch a misunderstanding than after a full implementation.

⚠️ Common mistake: Building a feature based on your own best guess at an ambiguous requirement, rather than surfacing the specific ambiguity for clarification first. The cost of a clarifying question up front is almost always smaller than the cost of reworking a built feature based on a wrong assumption.

Next Steps

The pattern across debugging, review, and refactoring: providing the specific failing case, your own hypothesis, or an explicit scope boundary consistently produces a more targeted and useful response than a general request. The context you provide about what you already know or suspect is often more valuable than the code itself.

If you're debugging or reviewing code regularly with your team, it's worth establishing this context-first habit as a shared norm -- always including the failing case, the hypothesis, or the scope boundary. PromptABCD works well for storing prompt templates that build this context-gathering step in by default, so it's not something each developer has to remember to do manually every time.