30 AI Coding Prompts That Actually Work (Tested on Real Projects)

Why Most AI Prompts Fail

"Fix this bug" is not a prompt — it's a wish. The AI has no context, no constraints, no expected output format. It guesses, and when it guesses wrong, you waste 20 minutes in a back-and-forth that should have taken 2.

The difference between a bad prompt and a good one is specificity. A good prompt tells the AI:

• Who it is — role and expertise level
• What it knows — language, framework, project conventions
• What you want — specific output format and constraints
• What good looks like — examples, edge cases to cover
• What to avoid — common mistakes, deprecated patterns

Below are 10 prompt templates that nail all five. Copy, fill in the brackets, and paste — no "no, not like that" back-and-forth needed.

DebuggingThe Root Cause Analysis Prompt

You are a senior [language] developer with 15 years of experience debugging production systems.

BUG REPORT:
- Symptom: [describe what's happening]
- Error message: [paste full error]
- Expected behavior: [what should happen]
- Environment: [OS, language version, relevant dependencies]
- First noticed: [when did this start? After what change?]

ANALYSIS PROCESS:
1. Read the error message character by character. What is it ACTUALLY saying? (Most developers misread error messages)
2. Trace backwards from the error to the root cause. Do NOT stop at the first symptom.
3. List the 3 most likely root causes, ranked by probability.
4. For each cause, explain WHY it would produce this exact error.
5. For cause #1, show the exact code fix as a diff.
6. Write a targeted test that would have caught this specific bug.
7. What pattern or practice would prevent this class of bug?

DebuggingThe Stack Trace Decoder

I have the following stack trace. Do NOT give me generic debugging advice. Instead:

1. Parse each frame — what function, what file, what line
2. Identify the actual point of failure (not the symptom)
3. Explain what data state would cause this path
4. Tell me what variable to inspect and what value to look for
5. Write the fix

Language: [language]
Framework: [framework]
Stack trace:
```
[paste full stack trace]
```
Relevant code:
```
[paste the function(s) referenced in the trace]
```

TestingThe Comprehensive Unit Test Generator

Write unit tests for this [language] function using [testing framework].

FUNCTION:
```
[paste function]
```

COVERAGE REQUIREMENTS:
- Happy path: all normal inputs produce correct outputs
- Edge cases: null, undefined, empty strings, empty arrays, 0, -1, MAX_INT
- Error cases: invalid inputs that should throw
- Boundary values: just above/below thresholds
- Concurrency: (if relevant) race conditions to test

For each test case, write:
1. A descriptive test name that explains the scenario
2. The Arrange-Act-Assert block
3. A comment explaining why this case matters

Use parameterized/table-driven tests where the framework supports it. Group related tests with describe/it blocks.

TestingThe Integration Test Designer

Design integration tests for: [describe feature/endpoint]

Tech stack: [backend framework, database, external services]

The tests should verify:
1. Full request/response cycle with real database
2. Authentication and authorization (valid token, invalid token, wrong role)
3. Data persistence (round-trip: create → read → verify)
4. Transaction rollback on partial failure
5. External service interaction (mock or sandbox)

For each test:
- Describe the setup (what data must exist before the test)
- Show the request (method, path, headers, body)
- Specify the expected response (status code, body shape, specific fields to check)
- List the teardown steps

Do NOT write tests that mock every dependency — use real infrastructure where possible.

Code GenerationThe Feature-First Code Generator

Implement this feature in [language/framework]:

FEATURE: [one-paragraph description of what the user can do]

CONSTRAINTS:
- Follow [project convention file / style guide]
- Use [specific libraries] for [specific concerns]
- Handle these error states: [list]
- Support these edge cases: [list]
- Performance budget: [e.g., < 200ms p95, < 50MB memory]

OUTPUT FORMAT:
1. File structure (which files to create/modify)
2. Main implementation with all business logic
3. Error handling and input validation
4. Type definitions (if typed language)
5. Brief notes on design decisions

Do NOT add comments that explain WHAT the code does — the code should be self-documenting. Only comment WHY for non-obvious decisions.

Code GenerationThe API Endpoint Builder

Build a REST API endpoint for: [operation description]

TECH STACK:
- Framework: [Express/Fastify/Django/Rails/etc.]
- Database: [PostgreSQL/MySQL/MongoDB]
- Auth: [JWT/OAuth2/API Key]

SPECIFICATION:
- Method: [GET/POST/PUT/DELETE]
- Path: [/api/v1/resource]
- Auth required: [yes/no]
- Rate limit: [requests/minute]

IMPLEMENTATION CHECKLIST:
1. Input validation (all fields, types, constraints)
2. Authentication/authorization check
3. Business logic
4. Database operation (with proper transaction handling)
5. Response formatting
6. Error responses for: 400 (bad input), 401 (unauthorized), 403 (forbidden), 404 (not found), 409 (conflict), 500 (server error)
7. Request/response logging

Show the complete implementation with all imports.

RefactoringThe Legacy Code Modernizer

Refactor this [language] code for modern standards while PRESERVING EXACT BEHAVIOR.

ORIGINAL CODE:
```
[paste legacy code]
```

REFACTORING RULES:
1. Extract functions longer than 25 lines into well-named subroutines
2. Replace magic numbers/strings with named constants
3. Use early returns to reduce nesting (max 3 levels deep)
4. Replace imperative loops with declarative alternatives where clearer
5. Add proper types (TypeScript/mypy/type hints)
6. Remove dead code and commented-out blocks
7. Simplify complex conditionals (extract to boolean functions if needed)

OUTPUT:
1. The refactored code
2. A brief summary of each change and why
3. Any behavioral risks introduced (if any)

IMPORTANT: Do NOT change function signatures used by external callers. Do NOT change the output for any input.

RefactoringThe Design Pattern Applicator

This [language] code has a structural problem. Diagnose it and apply the right design pattern.

CODE:
```
[paste code with structural issues]
```

ANALYSIS STEPS:
1. What is the structural problem? (God object? Tight coupling? Duplicated logic? Switch-on-type?)
2. Which design pattern best addresses it? Why this one?
3. Show the refactored code using the pattern
4. Compare: what becomes easier now? What (if anything) becomes harder?

Common patterns to consider: Strategy, Factory, Observer, Decorator, Command, Repository, Adapter, Template Method.

If no pattern is needed, say so — don't force one where it doesn't fit.

Code ReviewThe Security-First Code Reviewer

Review this code for security vulnerabilities. Be thorough and specific.

CODE:
```
[paste code to review]
```

AUDIT CHECKLIST:
1. Injection attacks: SQL, NoSQL, command, LDAP, XPath
2. XSS: reflected, stored, DOM-based
3. Authentication: session management, password handling, token storage
4. Authorization: privilege escalation, IDOR, missing access controls
5. Data exposure: PII, secrets, keys, tokens in code or logs
6. Input validation: missing or bypassable checks
7. CSRF: state-changing operations without tokens
8. File operations: path traversal, unrestricted uploads
9. Dependency security: known-vulnerable packages
10. Crypto: weak algorithms, hardcoded keys, wrong modes

For each finding:
- Severity: CRITICAL / HIGH / MEDIUM / LOW
- Location: filename and line number
- Description: what's wrong
- Exploit scenario: how an attacker would exploit this
- Fix: exact code change needed

PerformanceThe Database Query Optimizer

Optimize this slow [database] query.

TABLE SCHEMAS:
```sql
[paste CREATE TABLE statements for all relevant tables]
```

CURRENT QUERY:
```sql
[paste the slow query]
```

EXECUTION PLAN:
```
[paste EXPLAIN ANALYZE output]
```

TABLE SIZES:
- [table1]: [N] rows
- [table2]: [N] rows

ANALYSIS:
1. Point to the specific line(s) in the execution plan that indicate the bottleneck
2. Is it a scan (seq scan, full collection scan)? Should it be an index scan?
3. Are the JOINs ordered optimally?
4. Are there unnecessary columns being fetched?
5. Is the WHERE clause sargable?
6. Are there N+1 queries hiding in application code?

Provide:
1. The optimized query
2. CREATE INDEX statements for any new indexes needed
3. Expected improvement (rough percentage)
4. Any trade-offs (e.g., index write overhead, partial indexes vs full)