I've spent the last year refining my AI prompts for coding tasks. Most prompt guides are too generic. "Be specific" and "provide context" are obvious advice. Here are the actual prompts I use daily, with explanations for why they work.

The debugging prompt

This is my most-used prompt. When something breaks and I can't figure out why:

I have a bug. Here's what I expect to happen:
[expected behavior]

Here's what actually happens:
[actual behavior]

Here's the relevant code:
[paste code]

Here's the error message (if any):
[paste error]

Don't give me the fix immediately. First, list 3-5 possible causes
ranked by likelihood. Then suggest the fix for the most likely cause.

The key insight here is "don't give me the fix immediately." Without this, AI models jump to the first plausible solution. By forcing them to enumerate possibilities first, you get better diagnostic thinking. I've caught subtle bugs this way that a direct "fix this" prompt would have missed entirely.

The refactoring prompt

Refactor this code. Priorities in order:
1. Readability (someone new should understand it in 60 seconds)
2. Remove duplication
3. Simplify control flow

Keep the same function signatures. Don't change the public API.
Don't add features or change behavior.

[paste code]

The ordered priorities matter. Without them, AI tends to over-engineer, adding abstraction layers and design patterns that make the code "cleaner" by textbook standards but harder to actually read. The constraint about not changing the public API prevents the model from going on a refactoring adventure that breaks your callers.

The architecture prompt

I'm building [description]. Here are my constraints:
- Tech stack: [list]
- Scale: [expected users/data volume]
- Team size: [number]
- Timeline: [weeks/months]

Propose an architecture. For each component, explain:
1. What it does
2. Why this choice over alternatives
3. The biggest risk with this choice

Keep it pragmatic. I'd rather ship something simple that works
than design something perfect that takes forever.

That last line is critical. Without it, AI models default to enterprise-grade solutions with message queues, microservices, and Kubernetes for what could be a simple monolith serving 1,000 users. The constraints section grounds the response in reality.

The test generation prompt

Write tests for this function. Include:
1. Happy path (normal expected input)
2. Edge cases (empty input, null, boundary values)
3. Error cases (what should throw/fail)

Use [testing framework]. No mocking unless absolutely necessary.
Each test should test exactly one behavior.
Name tests: should_[expectedBehavior]_when_[condition]

[paste function]

The naming convention instruction is doing heavy lifting here. It forces the AI to think about what each test actually verifies before writing it, which results in more meaningful tests instead of random input combinations. The "no mocking unless necessary" prevents the model from creating an elaborate mock setup for a function that could be tested with simple inputs.

The code review prompt

Review this code as a senior engineer. Focus on:
1. Bugs or potential runtime errors
2. Performance issues with scale
3. Security concerns
4. Missing error handling

Don't comment on style or formatting.
Don't suggest changes that are purely preferential.
Only flag things that could cause real problems.

[paste code]

The "don't comment on style" instruction is essential. Without it, you get 15 suggestions about variable naming and bracket placement. I want the AI to find actual problems, not bikeshed.

The explanation prompt

Explain this code to me. Assume I'm a mid-level developer
who understands [language] but hasn't seen this codebase before.

Walk through the execution flow step by step.
Flag anything that's unusual, clever, or potentially confusing.
If there are implicit assumptions in the code, make them explicit.

[paste code]

I use this when onboarding onto unfamiliar codebases. The "mid-level developer" framing is important. If you don't set the level, you either get explanations that start with "a function is a reusable piece of code" or ones that assume you already understand the domain. Mid-level hits the sweet spot.

General principles I've learned

Tell the AI what NOT to do. Constraints improve output quality more than additional instructions. "Don't use any external libraries" is more effective than "try to keep dependencies minimal."

Specify the output format. "Give me a numbered list" or "respond with just the code, no explanation" saves you from having to parse through prose to find the answer.

Include your reasoning level. Saying "I already know X, don't explain that part" prevents the model from wasting tokens on things you understand.

These prompts work well with both Claude and GPT-4. Claude tends to follow the constraints more strictly, GPT-4 tends to be more concise in its responses. Use whichever model fits your workflow, but the prompt structures transfer across both.