March 21, 2026

30+ Dev Prompts That Actually Work

Count how many times today you typed some variation of "review this code for issues" or "explain why this test is failing." Go ahead, I'll wait. If you're like most developers using AI daily, the answer is embarrassing. We're all writing the same prompts from scratch, over and over, slightly differently each time, getting inconsistent results.

The Prompt Router dev library is a collection of prompts that actually hold up — specific enough to get good responses, generic enough to reuse across any project. Here's a walkthrough of every category and what to do with them.

Debugging

Debugging prompts are the ones you reach for in a panic. The library has prompts for explaining errors, finding logic bugs, and diagnosing flaky tests. The key is giving the AI enough context without dumping your entire codebase on it.

"Here is a bug report and the relevant code. Identify the root cause, explain why it happens, and suggest a fix. Bug: [describe the bug]. Code: [paste code here]"

That last detail — asking for why it happens, not just the fix — is what separates a prompt you learn from vs. one that just gives you a patch to copy-paste. For CI failures, there's a separate prompt that takes a full log and asks for the specific line causing the failure plus a fix. Claude tends to be particularly good at this kind of root-cause analysis.

Code Review

This is where AI earns its keep. A solid code review prompt will catch things you miss after staring at the same file for three hours. The library covers general quality review, security audits, and PR feedback.

"Review this code for correctness, readability, performance, and potential edge cases. Point out specific issues and suggest improvements. Language: [language]. Code: [paste code here]"

The security audit prompt is worth calling out separately — it specifically looks for injection vulnerabilities, insecure defaults, and data exposure. Run it before any code touches a production endpoint. Claude and GPT-4 both do well here; Claude tends to write more detailed explanations of why something is a problem.

Refactoring

Refactoring prompts are for the code that works but you're ashamed to show anyone. The library has prompts for improving readability, extracting reusable functions, and simplifying overly complex logic.

The readability prompt is deliberately simple: give it the code, tell it the language, and ask for cleaner structure with better naming. The complexity reduction prompt goes further — it specifically asks the AI to flag nested conditionals and propose flatter alternatives. Swap in your language and any style guide you follow and the results get noticeably sharper.

Testing

Writing tests is the task everyone agrees is important and nobody wants to do. These prompts make it faster.

"Write unit tests for the following function using [testing framework]. Cover happy path, edge cases, and error conditions. Function: [paste function here]"

Swap [testing framework] for Jest, pytest, RSpec, Go's testing package — whatever you're using. There's also an integration test prompt that asks the AI to think through system boundaries and external dependencies, and a coverage gap prompt that takes your existing tests and tells you what's missing. ChatGPT tends to generate boilerplate test scaffolding quickly; Claude tends to catch more subtle edge cases.

Architecture

Architecture prompts are for the bigger decisions: database schemas, API design, how to structure a new service. These are the prompts where you want to compare a few AI responses because the answers can vary significantly.

The schema design prompt asks for normalized tables with appropriate types, indexes, and constraints. The API design prompt asks for RESTful endpoints with request/response formats. Both have a [describe your requirements] slot where specificity pays off — the more context you give about scale, relationships, and constraints, the more grounded the output. For architecture brainstorming, running the same prompt through ChatGPT and Claude is genuinely useful because they often propose different tradeoffs.

DevOps

Dockerfile prompts, CI/CD pipeline prompts, infrastructure-as-code prompts. These are the ones that save you from copy-pasting from Stack Overflow and getting a 2019 answer.

"Write a Dockerfile for a [language/runtime] application. Use a multi-stage build, a non-root user, and optimize for a small final image. Requirements: [list your requirements]"

The CI/CD prompt takes your stack and outputs a working pipeline config for GitHub Actions or your CI of choice. The infrastructure prompt handles common patterns like VPC setup, auto-scaling groups, and load balancers. Gemini has been surprisingly solid for DevOps boilerplate — probably the Google-scale infrastructure knowledge showing through.

Git & Docs

The unglamorous category that makes your team love you. Commit message prompts, README prompts, changelog prompts. The commit message one is the most-used prompt in my rotation by a wide margin.

"Write a concise, conventional commit message for the following change. Use the format: type(scope): description. Change: [describe what changed]"

The README prompt asks for a full project overview with installation steps, usage examples, and configuration options. The changelog prompt diffs a list of commits into a human-readable format grouped by type. Any of the major models handle these well — this is where you pick whichever one you have open.

Customizing prompts that actually fit your stack

Every prompt in the library uses placeholders like [language], [testing framework], and [paste code here]. That's intentional. A few swaps that make a big difference:

The more specific the context, the less the AI has to guess. Guessing is where hallucinations live.

Which AI for which task

This is genuinely worth thinking about rather than defaulting to whichever tab you have open.

That's exactly why Prompt Router exists — so switching between them takes one click, not five tabs of copy-pasting.

Browse all 30+ prompts and send them to any AI in one click.

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