Most AI prompts for developers are useless. Not because the AI is bad - because the prompt is vague. "Review my code" gets you generic feedback. "Act as a senior engineer" gets you a persona, not a result. The prompts below are from the Prompt Playbook, a collection built around one principle: tell the model exactly what to check, in what order, with what output format. That specificity is what separates a useful response from a wall of generalities.
1. Production-Ready Code Review
Review this code for production readiness:
[LANGUAGE]
[PASTE CODE]
Review for:
1. BUGS: Actual errors, edge cases that would crash, off-by-one errors, null/undefined risks
2. SECURITY: Injection vulnerabilities, exposed secrets, missing input validation, auth issues
3. PERFORMANCE: N+1 queries, unnecessary loops, missing indexes, memory leaks, unoptimized algorithms
4. MAINTAINABILITY: Naming clarity, function length, single responsibility, magic numbers, dead code
5. ERROR HANDLING: Missing try/catch, swallowed errors, unhelpful error messages, missing retries for network calls
For each issue found:
- Line number or code snippet
- Severity: CRITICAL / WARNING / SUGGESTION
- Explanation of why it's a problem
- Fixed code snippet
If no issues in a category, say "No issues found" (don't manufacture problems).
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Why this works: Categorized review with severity levels mimics how senior engineers actually review code. The final instruction - "don't manufacture problems" - matters more than it looks. Without it, the model will nitpick working code to seem thorough. With it, a clean category means the category is clean.
2. Systematic Bug Hunting
I have a bug. Help me find it systematically.
What I expected: [EXPECTED BEHAVIOR]
What actually happens: [ACTUAL BEHAVIOR]
When it happens: [ALWAYS/SOMETIMES/ONLY WHEN...]
Error message (if any): [ERROR MESSAGE]
Recent changes: [WHAT CHANGED BEFORE THE BUG APPEARED]
Code involved:
[LANGUAGE]
[PASTE RELEVANT CODE]
Walk me through debugging this:
1. HYPOTHESES: List the 5 most likely causes, ranked by probability
2. NARROWING: For each hypothesis, what's the fastest way to confirm or eliminate it?
3. ROOT CAUSE: Based on the code, which hypothesis is most likely and why?
4. FIX: Proposed fix with code
5. PREVENTION: How to prevent this class of bug in the future (test to write, pattern to follow, linting rule to add)
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Why this works: The ranked hypothesis approach prevents the random "try this, try that" debugging loop that wastes hours. Structuring it as confirmation/elimination gives you a decision tree, not a guess. The prevention step is what transforms a one-off fix into a system improvement.
3. Inline Comment Writer (That Writes Useful Comments)
Add inline comments to this code. But NOT the obvious kind:
[LANGUAGE]
[PASTE CODE]
Comment rules:
- DO NOT comment WHAT the code does (the code already says that)
- DO comment WHY non-obvious decisions were made
- DO comment WHERE edge cases are handled and what they are
- DO comment WHEN the behavior differs from what a reader might expect
- DO comment WHO/WHAT this connects to (upstream callers, downstream effects, related modules)
- Add a file-level docstring explaining this module's role in the larger system
- For complex algorithms, add a one-paragraph explanation BEFORE the function, not scattered through it
Flag any code that should have a comment but you're not sure what the intent is: // TODO: Why does this [describe what the code does]?
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Why this works: "Comment why, not what" is the gold standard of code commenting - but most developers struggle to operationalize it. This prompt makes it concrete. The TODO format for unclear intent also turns documentation into a productive dialogue with the original author instead of a cover-up.
4. Regression Test Writer
I just fixed this bug: [BUG DESCRIPTION].
The fix was: [DESCRIBE THE FIX OR PASTE THE DIFF].
Write regression tests that:
1. Reproduce the original bug (this test should FAIL on the old code and PASS on the fix)
2. Test 3 variations of the same bug class (similar inputs that might trigger the same root cause)
3. Test that the fix didn't break the adjacent happy path
4. Test the boundary between "fixed behavior" and "unchanged behavior"
For each test, explain:
- What it tests
- Why this specific variation matters
- What a future developer should know if this test starts failing again
Framework: [TESTING FRAMEWORK]. Language: [LANGUAGE].
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Why this works: Testing three variations of the bug class - not just the exact reported case - catches the "same bug, different input" recurrences that plague codebases. The "future developer" notes make each test self-documenting, so when a test starts failing in six months, someone actually knows what it was protecting.
5. Technical Spec Writer
Write a technical specification for: [FEATURE/SYSTEM].
Inputs:
- What it needs to do: [REQUIREMENTS]
- Who will implement it: [TEAM/ROLE]
- Technical constraints: [EXISTING STACK, PERFORMANCE REQUIREMENTS, COMPATIBILITY]
- Timeline: [DEADLINE]
Sections:
1. OVERVIEW: What this is and why it's needed (3 sentences)
2. GOALS & NON-GOALS: Explicitly state what this spec covers AND what it intentionally excludes
3. PROPOSED SOLUTION: High-level design with component interaction description
4. DETAILED DESIGN: Data models, API contracts, key algorithms, error handling strategy
5. ALTERNATIVES CONSIDERED: Other approaches and why they were rejected
6. TESTING STRATEGY: What to test, how to test it, what "done" looks like
7. ROLLOUT PLAN: How to deploy safely (feature flags, canary, rollback plan)
8. OPEN QUESTIONS: Things that need resolution before or during implementation
Why this works: The NON-GOALS section is the underrated part. It prevents scope creep during implementation by making exclusions explicit before work starts. The OPEN QUESTIONS section acknowledges uncertainty rather than papering over it - which means the spec stays honest instead of becoming a liability when reality diverges.
These five prompts cover code review, debugging, documentation, testing, and spec writing - the recurring bottlenecks in most development workflows. They're part of a larger collection of 68 prompts across six categories (content, business, productivity, writing, creative, and technical).
The full Prompt Playbook is $7 at https://williamzero9.github.io/prompt-playbook/. One-time, no subscription, works with any major LLM.