10 Prompt Templates for Developers (Copy-Paste Ready)

Most developers use AI the same way: paste code, ask "what's wrong?" and hope for the best. It works sometimes. But a structured prompt gets better results every time — across GPT-4o, Claude, Gemini, and any other model you use.

Here are 10 copy-paste prompt templates for the tasks developers actually do every day. Each one is tested, practical, and works with any LLM. They apply the same principles covered in our guide on how to write better AI prompts.

1. Debug a Specific Error

When you paste a stack trace and say "fix this," the AI often rewrites your entire function. This template forces it to diagnose first, fix second.

I'm getting this error in my [language/framework] project:

[paste error message / stack trace]

Here's the relevant code:

[paste code]

Before suggesting a fix:
1. Explain what the error message means
2. Identify the exact line causing it
3. Explain WHY it's failing (root cause, not symptom)
4. Suggest the minimal fix — change as few lines as possible
5. Show how to verify the fix works

Why it works: Step 3 prevents the AI from pattern-matching the error to a common fix that doesn't apply to your case. Step 4 stops unnecessary rewrites. This step-by-step approach uses chain of thought reasoning to improve diagnostic accuracy.

2. Write a Function from Scratch

Vague: "write a function that processes payments." Better: tell the AI exactly what inputs, outputs, and edge cases to handle.

Write a [language] function that:

Purpose: [one sentence describing what it does]
Input: [parameter names, types, example values]
Output: [return type, example return value]
Edge cases to handle: [list 2-3 specific scenarios]
Constraints: [performance requirements, dependencies to use/avoid]

Include:
- Type annotations
- Input validation
- Error handling for [specific failure modes]
- One usage example

Do NOT include: [tests / comments / logging — specify what you don't want]

Why it works: Listing edge cases upfront is the single biggest improvement. Without them, the AI writes the happy path and ignores the rest.

3. Code Review

Don't ask "review my code." Tell the AI what to look for.

Review this [language] code for:

1. Bugs — logic errors, off-by-one, null/undefined risks
2. Security — injection, auth bypass, data exposure
3. Performance — unnecessary loops, N+1 queries, memory leaks
4. Readability — naming, structure, dead code

Code:
[paste code]

Context: This is a [API endpoint / utility function / React component]
that [what it does in production].

For each issue found, rate severity (critical / warning / nitpick)
and show the fix inline.

Why it works: The severity rating prevents the AI from treating a typo in a comment the same as an SQL injection vulnerability. The context line tells it what matters in production.

4. Write Unit Tests

Write unit tests for this [language] function using [test framework]:

[paste function]

Cover these scenarios:
- Happy path with typical input
- Empty / null / undefined input
- Boundary values (0, -1, max int, empty string)
- Error cases that should throw/reject
- [any domain-specific edge case]

Use descriptive test names that explain the expected behavior.
Each test should be independent — no shared mutable state.
Use [mock library] for external dependencies.

Why it works: Explicitly listing the scenarios prevents the AI from writing five tests that all check the happy path with slightly different inputs — the most common failure mode.

5. Explain Unfamiliar Code

You've inherited a codebase. You need to understand it fast.

Explain this code as if I'm a [junior/mid/senior] developer
who knows [your background] but has never seen this codebase.

[paste code]

Explain:
1. What this code does (one paragraph summary)
2. Walk through the execution flow step by step
3. Why it's written this way (design decisions, trade-offs)
4. What would break if I changed [specific part]
5. Any non-obvious dependencies or side effects

Why it works: "Explain this code" gets you a line-by-line paraphrase. This template gets you understanding — the why behind the what. Step 4 is especially useful when you're about to refactor something.

6. Refactor Without Changing Behavior

Refactor this [language] code to improve [readability / performance /
maintainability]. The external behavior must stay exactly the same.

Current code:
[paste code]

Requirements:
- Same inputs produce same outputs
- Same error handling behavior
- No new dependencies
- [specific constraint: "keep the public API identical"]

Show the refactored code, then list every change you made
and why. If any change COULD affect behavior, flag it explicitly.

Why it works: "Same inputs produce same outputs" is the key constraint. Without it, AI models love to "improve" code by changing return types, error formats, or parameter defaults — breaking callers silently.

7. Write a Database Query

Write a [SQL / MongoDB / etc.] query for this task:

Goal: [what data to retrieve/modify]
Tables/collections involved: [list with key columns]
Relationships: [how tables connect — foreign keys, joins]
Filters: [conditions, date ranges, status values]
Expected result: [describe shape — "list of users with their
order count, sorted by most orders"]

Constraints:
- Must work on [PostgreSQL 16 / MySQL 8 / etc.]
- Table has [X million] rows — optimize for performance
- [Any specific index hints or CTEs preferred]

Show the query, then explain the execution plan in plain English.

Why it works: "Explain the execution plan" forces the AI to think about performance, not just correctness. It'll often self-correct an inefficient query when it has to explain why it chose a sequential scan. If you need the query results in a specific format, see our guide on getting structured output from LLMs.

8. Write API Documentation

Write API documentation for this endpoint:

[paste route handler / controller code]

Include:
- Endpoint: method + path
- Description: one sentence, what it does
- Authentication: required? what type?
- Request: headers, query params, body (with types and example)
- Response: status codes (200, 400, 401, 404, 500) with
  example JSON for each
- Rate limits (if applicable)
- cURL example

Format as Markdown. Keep descriptions concise — developers
will skim this, not read it.

Why it works: Listing all status codes forces complete documentation. Most AI-generated docs only show the 200 response — this template captures error cases too. "Developers will skim this" sets the right tone.

9. Design a Database Schema

Design a [PostgreSQL / MySQL / MongoDB] schema for:

Application: [what the app does]
Core entities: [list the main objects — users, orders, products]
Key relationships: [one-to-many, many-to-many]
Must support these queries efficiently:
- [query 1: "get all orders for a user, sorted by date"]
- [query 2: "find products with low stock across warehouses"]
- [query 3: "monthly revenue report by category"]

Include:
- Table definitions with column types
- Primary keys, foreign keys, indexes
- Any constraints (unique, check, not null)
- Brief explanation of why you chose this structure over alternatives

Scale: [expected row counts — "~100K users, ~2M orders"]

Why it works: "Must support these queries efficiently" is the key line. Schema design without knowing the access patterns produces normalized-but-slow databases. This template designs for real usage.

10. Write a Git Commit Message

Write a commit message for these changes:

[paste git diff or describe changes]

Rules:
- Subject line: imperative mood, under 50 characters
- Blank line after subject
- Body: explain WHY the change was made, not WHAT changed
  (the diff already shows what)
- If it fixes a bug, describe the symptom users experienced
- If it's a feature, describe the user-facing behavior
- Reference issue number if applicable: "Fixes #123"

Why it works: "Explain WHY, not WHAT" is the rule most developers know but most AI-generated commit messages ignore. This template enforces it, producing messages that are actually useful when you git blame six months later.

Using These Templates Effectively

A few tips that apply to all ten:

Customize the brackets. Every [bracketed section] is a placeholder. The more specific your fill-in, the better the output. "[paste code]" with 200 lines is fine — these models handle large contexts well.

Chain them. Use template #1 to debug, then #3 to review your fix, then #4 to write tests for it. Each output feeds the next prompt.

Save what works. When a template produces great results for your specific stack, save that version. A "Django REST endpoint review" template will outperform a generic code review prompt.

Iterate once. If the output is 80% right, don't start over — tell the AI what to fix: "Good, but change the error handling to return 422 instead of 400 for validation errors." One follow-up beats re-prompting from scratch.

Skip the Manual Work

These templates work because they add structure, specificity, and constraints to your prompts — the same principles behind the STOKE framework. That's exactly what Promplify does automatically — submit any prompt and the optimizer adds the right framework, output structure, and constraints based on what you're asking for.

You can also save your favorite templates in the Prompt Library and reuse them across projects.

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Want these templates auto-optimized for your specific model and task? Try Promplify free — paste any developer prompt and get a structured, framework-enhanced version in seconds.