AI is not just a tool, it’s a pair programmer available 24/7. The difference from a regular programmer? Its capability heavily depends on how well you communicate with it.

That’s why prompt engineering has become an essential skill for developers. This article will give you ready-to-use templates for various everyday coding scenarios.

What is Prompt Engineering?

Prompt engineering is the art and science of effectively communicating tasks to AI. It’s not just about writing questions, but structuring instructions that are clear, organized, and complete with the context needed.

Imagine you’re briefing a freelancer for a project. The clearer the brief, the better the results. It’s exactly the same with AI.

Basic Principles of Prompt Engineering

1. Be Specific, Not Vague

❌ "Create a function to handle data"

✅ "Create a TypeScript function named `formatUserData` that:
   - Accepts an array of user objects with fields: id, name, email, createdAt
   - Returns an array with format: { displayName: string, emailMasked: string, joinDate: string }
   - Masks email to 'j***@gmail.com'
   - Formats date to 'DD MMM YYYY'"

2. Provide Context

AI knows nothing about your project. Tell it:

Framework/library being used
Version being used
Conventions applied in the codebase

3. Define Output Format

What form do you want the answer in? Code only? Step-by-step? Table? Specify in the prompt.

4. Iterate and Refine

The first prompt is rarely perfect. Treat this as a conversation, not a one-shot query.

Anatomy of a Good Prompt

Every effective prompt has these 4 components:

┌─────────────────────────────────────────────────────────────┐
│  CONTEXT                                                     │
│  "I'm developing a Next.js 14 app with TypeScript..."       │
├─────────────────────────────────────────────────────────────┤
│  TASK                                                        │
│  "Create a custom hook for infinite scroll..."               │
├─────────────────────────────────────────────────────────────┤
│  CONSTRAINTS                                                 │
│  "Use React Query, handle loading/error states..."          │
├─────────────────────────────────────────────────────────────┤
│  OUTPUT FORMAT                                               │
│  "Provide complete code with JSDoc comments..."              │
└─────────────────────────────────────────────────────────────┘

Let’s look at a real example:

## Context
I'm developing an e-commerce app with Next.js 14 (App Router), 
TypeScript, and Tailwind CSS. State management uses Zustand.

## Task  
Create a shopping cart feature that persists to localStorage.

## Constraints
- Must be type-safe (full TypeScript)
- Handle edge cases: item out of stock, max quantity
- Optimistic updates for smooth UX
- Sync with backend when online

## Output Format
1. Zustand store definition
2. Custom hooks for cart access
3. Usage example in component

Prompt Templates for Developers

🐛 Template: Debugging

## Problem
[Brief description of the issue]

## Error Message

[Paste complete error message]


## Relevant Code
```[language]
[Paste problematic code - include imports]

Environment

Framework: [Next.js 14 / React 18 / etc]
Runtime: [Node 20 / Bun / etc]
Related packages: [package@version]

What I’ve Tried

[Steps already attempted]
[And their results]

Expected vs Actual Behavior

Expected: [What should happen]
Actual: [What actually happens]


**Example usage:**

```markdown
## Problem
API route returns 500 error when fetching products

## Error Message

TypeError: Cannot read properties of undefined (reading ‘map’) at GET (app/api/products/route.ts:15:23)


## Relevant Code
```typescript
import { prisma } from '@/lib/prisma'

export async function GET() {
  const products = await prisma.product.findMany()
  
  const formatted = products.data.map(p => ({
    id: p.id,
    name: p.name
  }))
  
  return Response.json(formatted)
}

Environment

Next.js 14.0.4
Prisma 5.7.0
Node 20

What I’ve Tried

Check database connection - OK
Run prisma.product.findMany() in separate script - works

Expected vs Actual

Expected: Return JSON array of products
Actual: 500 Internal Server Error


### 🔍 Template: Code Review

```markdown
## Code for Review
```[language]
[Paste code]

Context

This is [type of feature/component]
Will be deployed to [production/staging]
Expected traffic: [info if relevant]

Review Focus

Please review from these perspectives:

⚡ Performance - any bottlenecks?
🔒 Security - any vulnerabilities?
🧹 Code quality - readability, maintainability
🐛 Bug potential - missed edge cases?
📏 Best practices - following [framework] conventions?

Output Format

For each issue, provide:

🔴 Critical / 🟡 Warning / 🟢 Suggestion
Issue location (line/function)
Explanation of why this is an issue
Example of fixed code


### ♻️ Template: Refactoring

```markdown
## Current Code
```[language]
[Paste code to refactor]

Problems with This Code

[Problem 1: e.g., too many responsibilities]
[Problem 2: e.g., hard to test]
[Problem 3: e.g., duplicate logic]

Refactoring Goals

[Goal 1: e.g., split into smaller functions]
[Goal 2: e.g., add proper error handling]
[Goal 3: e.g., make more testable]

Constraints

Don’t change public API / function signature
Stay compatible with [existing code]
Use pattern: [e.g., Repository pattern]

Output

Refactored code with comments explaining changes
Before/after comparison for each major change
Potential breaking changes (if any)


### 🏗️ Template: Generate Boilerplate

```markdown
## What to Generate
[Type of boilerplate: API route, React component, etc]

## Specifications
- Name: [file/component name]
- Location: [path in project]
- Purpose: [what it does]

## Tech Stack
- Framework: [Next.js / Remix / etc]
- Language: [TypeScript / JavaScript]
- Styling: [Tailwind / CSS Modules / etc]
- State: [Zustand / Redux / etc]
- Validation: [Zod / Yup / etc]

## Features Required
1. [Feature 1 with details]
2. [Feature 2 with details]
3. [Feature 3 with details]

## Code Style
- [Conventions: e.g., named exports, functional components]
- [Formatting: e.g., single quotes, trailing commas]

## Example of Similar Code in Codebase
```[language]
[Paste example of similar code from project for style reference]


**Example usage:**

```markdown
## What to Generate
React component for product card in e-commerce

## Specifications
- Name: ProductCard
- Location: components/product/ProductCard.tsx
- Purpose: Display product info on listing page

## Tech Stack
- Next.js 14 (App Router)
- TypeScript (strict mode)
- Tailwind CSS
- Zustand for cart state

## Features Required
1. Display: image, title, price, rating, discount badge
2. Hover state: quick add to cart button appears
3. Skeleton loading state
4. Responsive: 2 cols mobile, 4 cols desktop

## Code Style
- Named exports
- Interface for props in same file
- Tailwind classes, no inline styles
- Compound component pattern if sub-components needed

📝 Template: Write Documentation

## Code to Document
```[language]
[Paste code]

Documentation Type

[JSDoc / README / API docs / Inline comments]

Audience

[Junior devs / External API consumers / Internal team]

Documentation Should Include

Format

[Markdown / JSDoc / TSDoc]

Example of Existing Docs in Project

[Paste example of existing documentation]


### 🎓 Template: Explain Code

```markdown
## Code to Explain
```[language]
[Paste code]

My Understanding Level

[Beginner / Intermediate / Advanced] in [language/framework]

Specific Questions

[Specific question 1]
[Specific question 2]

Explanation Format

Please explain with:

Brief summary (1-2 sentences)
Step-by-step breakdown
Simple analogy (if concept is complex)
Diagram/visual (if applicable)
Common use cases
Potential pitfalls


### 🔄 Template: Convert Code Between Languages

```markdown
## Source Code
```[source_language]
[Paste original code]

Convert To

[target_language] with [framework if any]

Maintain

Target Environment

Runtime: [Node / Deno / Bun / Browser]
Version: [target_language version]
Dependencies available: [list libraries that can be used]

Additional Requirements

[Requirement 1: e.g., async/await style]
[Requirement 2: e.g., specific naming convention]


**Example usage:**

```markdown
## Source Code
```python
def fetch_user_data(user_id: str) -> dict:
    try:
        response = requests.get(f"{API_URL}/users/{user_id}")
        response.raise_for_status()
        data = response.json()
        return {
            "id": data["id"],
            "name": data["full_name"],
            "email": data["email_address"]
        }
    except requests.RequestException as e:
        logger.error(f"Failed to fetch user: {e}")
        raise UserFetchError(f"Could not fetch user {user_id}")

Convert To

TypeScript with modern Node.js

Maintain

Same functionality
Idiomatic TypeScript style
Proper error handling

Target Environment

Runtime: Node 20
Available: fetch (native), zod for validation

Additional Requirements

Use native fetch (no axios)
Return type must be strongly typed (no any)
Add input validation


## Chain Prompting for Complex Tasks

For large tasks, break them into sequential prompts:

### Example: Build Feature End-to-End

**Prompt 1: Planning**
```markdown
I want to build a comment system for a blog with:
- Nested replies (max 3 levels)
- Like/unlike comments
- Real-time updates
- Markdown support

Tech stack: Next.js 14, Prisma, PostgreSQL

Please create:
1. Database schema design
2. Required API endpoints
3. Component hierarchy
4. State management approach

Don't write code yet, focus on architecture.

Prompt 2: Implementation (after approving architecture)

Based on the architecture above, now implement:
1. Prisma schema for comments

[Paste architecture from previous prompt for context]

Prompt 3: Continue Implementation

Now implement API routes for:
- GET /api/comments (with pagination)
- POST /api/comments (create new)
- PATCH /api/comments/:id (edit)
- DELETE /api/comments/:id

Use schema and conventions from before:
[Paste relevant code from previous prompt]

Prompt 4: Testing

Create test cases for the API routes that were created:
[Paste API route code]

Testing framework: Vitest
Mocking: msw for API calls
Coverage target: happy path + error cases

Tips: Iterate and Refine Prompts

Technique 1: Effective Follow-ups

You: [Initial prompt]
AI: [Response with code]

You: "Good, but please:
1. Add loading state
2. Handle case when array is empty
3. Separate styles to separate file"

Technique 2: Ask for Alternatives

"Give 3 different approaches to solve this, with pros/cons of each"

Technique 3: Challenge AI

"What are potential issues with this approach? 
How would you handle it if the data is very large?"

Technique 4: Ask for Improvement

"You're a senior developer. Review the code you just gave. 
What can be improved?"

Common Mistakes and How to Avoid Them

❌ Mistake 1: Prompt Too Short

"Fix the bug in this code"

Why it’s bad: AI doesn’t know the context, what error, what environment.

✅ Fix: Use the debugging template above.

❌ Mistake 2: Too Many Tasks at Once

"Create a complete authentication system with login, register, 
forgot password, OAuth, role-based access, and audit logging"

Why it’s bad: AI gets overwhelmed, result will be shallow and incomplete.

✅ Fix: Split into multiple prompts with chain prompting.

❌ Mistake 3: Not Specifying Tech Stack

"Create an API for user management"

Why it’s bad: AI might generate using a different framework from your project.

✅ Fix: Always include tech stack and version.

❌ Mistake 4: Copy-Paste Without Review

AI can generate code that:

Uses deprecated APIs
Has security vulnerabilities
Isn’t efficient at scale
Doesn’t match project conventions

✅ Fix: ALWAYS review, test, and understand code before committing.

❌ Mistake 5: Not Providing Examples

"Create a component with the same style as the others"

Why it’s bad: AI can’t read your codebase (unless using Cursor @codebase).

✅ Fix: Paste similar example code as reference.

❌ Mistake 6: Expecting AI to Be Perfect on First Try

✅ Fix: Treat it as a conversation. Iterate and refine.

Quick Reference: Prompt Starters

Use Case	Prompt Starter
Debug	”I’m getting error [X] when [Y]. Code: …”
Explain	”Explain step-by-step how this code works: …”
Refactor	”Refactor this code for [goal]. Constraints: …”
Generate	”Create [what] with specs: …”
Review	”Review this code from [aspects] perspective: …”
Convert	”Convert this code from [A] to [B]: …”
Optimize	”Optimize this code for [performance/readability]: …”
Test	”Create test cases for this code with [framework]: …”

Conclusion

Prompt engineering isn’t magic, it’s a skill that can be developed. Key takeaways:

Structure matters - Use the CONTEXT + TASK + CONSTRAINTS + OUTPUT format
Be specific - The more detailed the prompt, the better the output
Iterate - First prompt is rarely perfect, and that’s OK
Provide examples - Paste existing code as reference
Chain for complexity - Break large tasks into steps
Always review - AI is an assistant, not a replacement

Start with the templates above, customize as needed, and keep refining based on experience.

Templates in this article can be bookmarked and used directly. Have your own favorite template? Share on Twitter @nayakayp!