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Coding Prompts

AI prompts for debugging, code review, explaining concepts, and writing better code — 25 ready-to-use prompts, copy with one click.

How AI Coding Assistants Are Changing Software Development in 2026

AI coding assistants have moved from novelty to standard professional tool in under three years. GitHub Copilot, Cursor, ChatGPT, and Gemini are now embedded in the daily workflows of developers at every level. But the gap between developers who use AI effectively and those who do not comes down entirely to prompt quality.

These 25 prompts are built for real development scenarios — debugging production errors, refactoring legacy code, writing unit tests, explaining complex algorithms, and designing system architecture. Each prompt is pre-filled with a specific stack context: a React + Node.js SaaS application, a Python FastAPI microservice, a Unity C# XR experience, or a PostgreSQL schema optimisation problem.

Whether you are a junior developer learning new concepts, a senior engineer reviewing a pull request, or a solo founder building an MVP — these prompts will make your AI coding sessions significantly more productive and your code significantly cleaner.

How to Use These Prompts Effectively

1

Paste your actual code with the prompt

Include your real code, error message, or requirements directly. Context is everything for useful coding assistance.

2

Use GitHub Copilot for active development

Copilot in VS Code or Cursor is the most productive for line-by-line assistance during active coding sessions.

3

Use ChatGPT for complex analysis and architecture

ChatGPT handles larger codebases and complex architecture questions better. Use it for system design and thorough code review.

4

Always test AI-generated code before production

Run AI output through your linter and tests. AI code is a strong starting point — not production-ready without review.

💡 Pro Tips

  • Always include the full error message. For debugging prompts, paste the complete stack trace — not just the error type. Line numbers and call stack tell the AI exactly where to look.
  • Use GitHub Copilot for active coding, ChatGPT for architecture. Copilot in VS Code or Cursor is most productive for line-by-line assistance. ChatGPT handles complex multi-file architecture and code review better.
  • Specify language and framework version. "Python 3.11 with FastAPI 0.110" gives dramatically better results than just "Python". Version-specific syntax varies significantly.
  • Run the Code Reviewer prompt before every PR. Paste your diff before opening a pull request. It catches issues human reviewers miss — edge cases, security vulnerabilities, and performance bottlenecks.
👤

Curated by Prabhu Kumar Dasari

Senior XR Developer · AI Architect · 13+ years experience · Building XR and AI applications in Unity C#, Python, and JavaScript since 2011

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All Coding Prompts
25 prompts
Code Explainer
Beginner
📌 Learning & teaching
Explain the following code in simple terms that a beginner can understand. Break down: what each section does, why certain approaches were chosen, any important concepts used, and potential issues or improvements. [PASTE YOUR CODE HERE]
Bug Finder & Fixer
Beginner
📌 Debugging
Review the following code and identify all bugs, errors, and issues. For each issue found: explain what the bug is, why it's a problem, and provide the corrected code. Also suggest any improvements for performance, readability, or security. Language: [language]. [PASTE YOUR CODE HERE]
🤖 Example tool: 🐛 ChatGPT
Node.js Express API returning 500 errors intermittently on a Razorpay payment webhook handler in production.
📄 ChatGPT Output
ChatGPT Debug Analysis
Error pasted:
TypeError: Cannot read properties of undefined (reading 'amount') at processPayment (webhookHandler.js:47:28)

ChatGPT diagnosis: The bug is a race condition — your Razorpay webhook handler is not validating event type before destructuring. When Razorpay sends a payment.captured event the structure is correct. But your handler also receives refund.created events where data.object has a different schema — no amount field at the top level.

Fix:
const processPayment = (payload) => { if (payload.event !== 'payment.captured') return; // guard clause const amount = payload.data.object.amount; // now safe

Security issue also flagged: Missing Razorpay.validateWebhookSignature() — anyone can send fake payment events to your endpoint without this.
Function Generator
Beginner
📌 Code generation
Write a [language] function that [describe what it should do]. Requirements: [list requirements]. The function should handle edge cases including [list edge cases]. Include: proper error handling, clear variable names, and comments explaining complex logic. Also write 3 unit tests.
Code Converter
Intermediate
📌 Cross-language development
Convert the following code from [source language] to [target language]. Maintain the same logic and functionality. Use idiomatic patterns for the target language. Add comments where the translation isn't straightforward. [PASTE YOUR CODE HERE]
API Integration Guide
Intermediate
📌 API development
Write code to integrate [API name] into a [language/framework] application. Include: authentication setup, making GET and POST requests, handling responses and errors, rate limiting considerations, and a practical example of [specific use case]. Add comments throughout.
Database Query Writer
Intermediate
📌 Database development
Write a [SQL/MongoDB/PostgreSQL] query to [describe what you need]. Requirements: [list requirements]. Consider: performance optimisation, proper indexing, handling NULL values, and security against injection. Also explain the query and suggest any relevant indexes.
React Component Builder
Intermediate
📌 Frontend development
Create a React functional component for [component description]. Requirements: [list props and behaviour]. Use: TypeScript, proper prop types, useState/useEffect where needed, and Tailwind CSS for styling. Include: loading state, error state, and empty state handling. Add JSDoc comments.
🤖 Example tool: ⚛️ GitHub Copilot
Sortable employee data table with pagination and CSV export for an Indian HR SaaS product.
📄 GitHub Copilot Output
GitHub Copilot Output in VS Code
Prompt typed as comment in VS Code:
// React component: sortable employee table with pagination // Props: employees[], columns[], pageSize=20 // Features: click header to sort, CSV export, row selection // Stack: React 18, TypeScript, Tailwind CSS

Copilot generated the complete component in ~8 seconds including:
• useMemo for sorted/paginated data to avoid re-renders
• useState for sort direction and current page
• CSV export using URL.createObjectURL() — no library dependency
• Row selection with shift-click multi-select
• TypeScript generics so the component works with any data shape

Manual fix needed: Copilot used English decimal formatting. Added toLocaleString("en-IN") for proper rupee formatting (₹1,234.56). 30-second fix saved approximately 45 minutes of component writing.
REST API Builder
Intermediate
📌 Backend development
Create a REST API endpoint in [language/framework] for [describe functionality]. Include: route definition, request validation, business logic, error handling with proper HTTP status codes, and response formatting. Also write the corresponding unit tests.
Code Reviewer
Intermediate
📌 Code quality
Perform a thorough code review of the following code. Evaluate: code quality and readability, performance issues, security vulnerabilities, error handling, test coverage gaps, adherence to SOLID principles, and potential edge cases. Provide specific, actionable feedback. [PASTE YOUR CODE HERE]
Algorithm Explainer
Intermediate
📌 Learning algorithms
Explain the [algorithm name] algorithm in simple terms. Include: what problem it solves, step-by-step walkthrough with a simple example, time and space complexity analysis, when to use it vs alternatives, and a clean implementation in [language].
Unit Test Writer
Intermediate
📌 Testing
Write comprehensive unit tests for the following [language] code using [testing framework]. Cover: happy path scenarios, edge cases, error cases, and boundary conditions. Use descriptive test names, arrange/act/assert pattern, and mock external dependencies. Aim for 90%+ code coverage. [PASTE YOUR CODE HERE]
🤖 Example tool: ✅ ChatGPT
Jest unit tests for a Python FastAPI payment processing microservice — needed 90%+ coverage for a Series A fintech QA gate.
📄 ChatGPT Output
ChatGPT Test Generation
Function to test:
async def process_upi_payment(amount: float, vpa: str, merchant_id: str) -> PaymentResult:

ChatGPT generated 8 test cases:
1. ✅ Happy path — valid UPI VPA, amount within limits, merchant active
2. ❌ Invalid VPA format — missing @ symbol
3. ❌ Amount below ₹1 minimum
4. ❌ Amount above ₹1,00,000 UPI daily cap
5. ❌ Merchant ID not found
6. ❌ Merchant account suspended
7. ⏱ NPCI gateway timeout after 30s
8. 🔄 Duplicate transaction ID returns cached result

Edge case ChatGPT flagged: "Your function doesn't handle VPA resolving but linked bank account inactive (NPCI error code 99). Common UPI failure mode — I've included a test case and suggested a specific exception class." Coverage went from 61% to 94%.
Regex Generator
Intermediate
📌 Pattern matching
Write a regular expression to [describe what pattern to match]. Requirements: [list what should match and what should not match]. Provide: the regex pattern, a plain English explanation of what it does, examples of matching strings, examples of non-matching strings, and code to use it in [language].
Performance Optimiser
Advanced
📌 Performance
Analyse the following code for performance issues and rewrite it to be more efficient. Identify: bottlenecks, unnecessary loops or operations, memory inefficiencies, and missing caching opportunities. Show before and after with explanation of improvements and expected performance gain. [PASTE YOUR CODE HERE]
System Design Helper
Advanced
📌 System design
Design a system architecture for [describe the system]. Include: high-level architecture diagram description, key components and their responsibilities, data flow, database schema, API design, scalability considerations, potential bottlenecks, and technology stack recommendations. Scale: [expected users/requests].
🤖 Example tool: 🏗️ ChatGPT
Real-time cricket score notification system needing to handle 50M concurrent users during IPL finals.
📄 ChatGPT Output
ChatGPT System Design Output (excerpt)
Scale requirements: 50M concurrent users, score updates every 30 seconds, 99.9% uptime, sub-500ms notification delivery

1. Score Ingestion Layer — Dedicated scraper cluster pulling from BCCI API. Runs on 3 AWS EC2 instances with automatic failover. Score changes published to Apache Kafka topic cricket.live.scores.

2. Fan-out Service — Kafka consumer reads updates, queries user subscription database (Redis cluster) to find which users follow that match. Publishes personalised notifications to per-region SQS queues.

3. Push Notification Delivery — Firebase Cloud Messaging for Android (~70% of Indian mobile users), APNs for iOS. Batch notifications in groups of 500 to stay within FCM rate limits.

Bottleneck ChatGPT identified: "At 50M users, notification fan-out is the bottleneck — not score ingestion. Pre-warm SQS queues 2 hours before match start and implement exponential backoff with jitter on FCM retries. Without jitter, retry storms after a brief FCM outage will amplify traffic 10-15x."
Security Auditor
Advanced
📌 Security
Perform a security audit of the following code. Check for: SQL injection vulnerabilities, XSS vulnerabilities, authentication/authorisation flaws, sensitive data exposure, insecure dependencies, CSRF vulnerabilities, and any other OWASP Top 10 issues. Provide severity rating and fix for each issue. [PASTE YOUR CODE HERE]
Docker Setup Generator
Advanced
📌 DevOps
Create a complete Docker setup for a [language/framework] application. Include: Dockerfile with multi-stage build, docker-compose.yml with [services needed], environment variable management, volume configuration, networking setup, and a README with build and run commands. Production-ready configuration.
CI/CD Pipeline Builder
Advanced
📌 DevOps
Create a [GitHub Actions/GitLab CI/Jenkins] CI/CD pipeline for a [language/framework] application. Include: linting, unit tests, integration tests, security scanning, build, and deployment to [environment]. Add proper caching, parallel jobs where possible, and failure notifications.
Refactoring Assistant
Advanced
📌 Code quality
Refactor the following code to improve maintainability, readability, and adherence to clean code principles. Apply: meaningful naming, single responsibility principle, DRY principle, proper abstraction, and design patterns where appropriate. Explain each refactoring decision. [PASTE YOUR CODE HERE]
Error Handler Generator
Intermediate
📌 Reliability
Write comprehensive error handling for the following [language] code. Cover: expected errors, unexpected errors, network failures, timeout handling, retry logic with exponential backoff, user-friendly error messages, error logging, and graceful degradation. [PASTE YOUR CODE HERE]
Documentation Writer
Intermediate
📌 Documentation
Write complete technical documentation for the following code. Include: overview and purpose, installation/setup instructions, API reference with all parameters and return types, usage examples, configuration options, troubleshooting guide, and changelog template. Format: Markdown. [PASTE YOUR CODE HERE]
Git Commit Message Generator
Beginner
📌 Version control
Generate a clear, conventional git commit message for the following code changes. Follow Conventional Commits format (type: description). Types: feat, fix, docs, style, refactor, test, chore. Include: short description (50 chars max), longer body if needed, and breaking change notes if applicable. Changes: [describe changes].
Mock Data Generator
Beginner
📌 Testing & development
Generate realistic mock data for [describe data type/schema]. Create [X] records with: realistic values, proper data types, relationships between fields, edge cases included, and export in [JSON/CSV/SQL] format. Schema: [describe or paste schema].
Code Comment Generator
Beginner
📌 Documentation
Add clear, helpful comments to the following code. Include: file-level documentation, function/method JSDoc comments with parameter and return descriptions, inline comments for complex logic, and TODO comments for known issues. Don't over-comment obvious code. Language: [language]. [PASTE YOUR CODE HERE]
Interview Prep — Coding
Intermediate
📌 Interview preparation
Give me a [difficulty: easy/medium/hard] coding interview question related to [topic: arrays/trees/dynamic programming/graphs etc.]. Include: problem statement, constraints, examples with input/output, hints (hidden), optimal solution with explanation, time/space complexity, and follow-up questions an interviewer might ask.
Unity C# Script Generator
Intermediate
📌 Unity development
Write a Unity C# script for [describe the behaviour/feature]. Requirements: [list requirements]. The script should: follow Unity best practices, use [URP/HDRP/Standard] render pipeline, handle Start/Update/OnDestroy lifecycle properly, include null checks, and have clear comments. Platform: [PC/Mobile/VR/AR].
Other Categories
✍️ Writing📣 Marketing🎨 Image Generation💼 Business🥽 XR & Unity📈 Stock Market & Finance💼 Resume & Jobs
Frequently Asked Questions

How do AI coding prompts differ from just asking ChatGPT to write code?

Generic requests produce generic code that rarely fits your stack or conventions. Structured coding prompts specify the language version, framework, existing code context, and error messages. This transforms AI from a code generator into a genuine pair programmer that understands your specific situation and produces output you can actually use without significant rewriting.

Which AI tool works best for different coding tasks?

GitHub Copilot in VS Code or Cursor is most productive for active line-by-line coding assistance — it understands your codebase context. ChatGPT handles complex multi-file architecture analysis and thorough code reviews better. Gemini Code Assist is strong for Google Cloud and Firebase-specific development. The coding prompts in this collection are designed primarily for ChatGPT for architecture and review, Copilot for active code generation.

How do I use these prompts safely for production code?

Treat all AI-generated code as a first draft requiring review — never deploy directly to production without testing. Run it through your linter, check for security vulnerabilities, and write tests before deploying. For security-sensitive functions — payment processing, authentication, data encryption — have a senior developer review AI output regardless of how correct it appears.

Can these coding prompts help junior developers learn faster?

Yes — the Code Explainer, Algorithm Explainer, and Code Reviewer prompts are particularly valuable for learning. Always use the "explain with inline comments" instruction when learning a new concept. The Code Reviewer prompt is excellent as a learning tool: paste your own code and ask AI to identify improvements with explanations. This creates a feedback loop similar to having a senior developer review your work daily.

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