AI Prompts for Software Developers: The Ones That Actually Save You Time

Most "AI prompts for developers" lists hand you the obvious stuff: write a function, generate a test. Useful, but you already knew AI could do that. What actually changes your week are the handful of prompts that use AI to pressure-test your thinking and catch what you missed, so those are the ones I am leading with. Change the details in brackets and they are yours.

One rule before we start: check your company's policy and never paste secrets, credentials, or customer data into a tool you do not control. When a snippet is sensitive, reduce it to a generic version that captures the problem without the proprietary details. If you want the fuller version of how to talk to these tools well, that is much of what my book, The Beginner's Guide to the AI Galaxy, is about.

The five most developers never think to try

Get reviewed by the harshest engineer on your team. This catches what you would get dinged for before anyone else sees it.

Review this code as the harshest senior engineer on my team — the one who blocks PRs over edge cases, error handling, naming, and hidden coupling. Do not tell me it looks good. List every specific thing you would leave a comment on, worst first, with why it matters. Code: [paste it].

Argue that your architecture is wrong. You do not want to discover the flaw six months into the build.

I am planning to [use a message queue between these two services instead of a direct call]. Argue as hard as you can that this is the wrong choice for [my case: two internal services, low traffic, one team]. Give me the strongest case against it, the failure modes I am not seeing, and what you would do instead. I want this pressure-tested before I commit.

Explain the legacy code you inherited, and flag the landmines. This turns a scary file into a map.

Explain this code I inherited as if I am new to the codebase. Walk me through what it actually does, then mark every part that looks risky, fragile, or like it is doing something non-obvious — the places I should be careful before I change anything. Code: [paste it].

Generate the edge cases you forgot. This is the list you write after the bug report, except before.

Here is a function that [parses user-supplied dates]. List the edge cases and failure modes I probably did not handle — empty input, malformed data, boundaries, concurrency, locale, whatever applies. Sort by how likely they are to actually bite me in production. Function: [paste it].

Rubber-duck the bug instead of asking for the fix. Say it out loud and you often solve it yourself.

Do not try to fix this bug. Instead, ask me the questions a good debugger would ask — what changed most recently, what I have already ruled out, what the smallest failing case is, what I expected versus what happened. One question at a time, and adjust based on my answers. Here is the symptom: [describe it].

The everyday time-savers

These are the staples. They will not surprise you, but they will give you your afternoons back.

Write the tests you keep putting off.

Write [unit tests] for this function using [Jest]. Cover the happy path, the edge cases, and the error cases, and match the style of this existing test file: [paste one]. Name each test so the intent is obvious from the failure output. Function: [paste it].

Explain a gnarly regex, or build one.

Explain what this regex matches in plain English, step by step, and give me two example strings it accepts and two it rejects: [paste it]. Then write a version that also [allows an optional country code].

Draft the commit message.

Write a commit message for this diff following Conventional Commits. One-line summary under 72 characters, then a body explaining why, not just what. Diff: [paste it].

Port it to another language.

Convert this [Python] function to [idiomatic TypeScript] — not a literal line-by-line translation, but the way someone fluent in the target language would actually write it. Keep the behavior identical and note anything that does not map cleanly. Code: [paste it].

Write the docstring or the comment that explains the why.

Write a [docstring] for this function describing what it does, its parameters, its return value, and any side effects or gotchas a caller should know. Do not restate the obvious; explain the why where it is not clear from the code. Function: [paste it].

Draft the PR description.

Write a pull request description for this change. Sections: what and why, how to test it, and anything reviewers should look at closely or that I am unsure about. Keep it tight. Here is the diff and the ticket it closes: [paste].

Turn a stack trace into a starting point.

Here is a stack trace and the relevant code. Give me the three most likely causes in order, what evidence in the trace points to each, and the fastest thing to check first for each one. Do not guess a fix yet. Trace: [paste]. Code: [paste].

How to make any of these yours

These prompts are starting points, not magic words. The single biggest upgrade is telling it your real context — the language, the framework, the constraints, the part you are unsure about — the way you would brief a new teammate before a review. The more of your actual situation you put in, the better it comes back, which is the whole idea behind talking to AI like a person, not a search box. And if a first answer is close but not right, do not start over, just tell it what to fix, one of the small prompting moves that change everything.

If you are choosing a tool to run these in, the coding assistants compared is a good place to start, and when a prompt graduates from your editor into something your app relies on, prompt engineering for production covers the parts that matter once real users are on the other end.

None of this replaces the part only you can do, which is knowing the system and owning what you ship. It clears the busywork off your plate so you have more of yourself left for the hard problems. That is the honest promise of AI for a developer, and it is a good one.