Introduction
If you are a developer using ChatGPT or GPT-4 daily, you already know that the quality of output depends almost entirely on the prompt. A vague request yields a generic answer; a well-structured prompt can produce production-ready code, detailed debugging sessions, or even high-level architectural designs. This article collects 15 proven prompts organized by difficulty — basic, advanced, and expert — that I have refined over two years of working with large language models. Each prompt includes the exact task, the prompt text, and a concrete example of the result you can expect. Whether you are a junior developer fixing your first bug or a senior engineer planning a microservices migration, these prompts will save you time and improve the quality of AI-assisted development.
Why Prompt Engineering Matters in 2026
By mid-2026, GPT-4 and its successors have become standard tools in many software teams. According to a 2025 survey by Stack Overflow, over 60% of professional developers use AI coding assistants at least weekly. However, the difference between a helpful answer and a misleading one often comes down to how you frame the request. Research from OpenAI (documented in their official prompt engineering guide at platform.openai.com) shows that explicit instructions, role assignment, and output formatting consistently improve accuracy. The prompts below follow these principles, using techniques like chain-of-thought reasoning, context injection, and constrained output.
Basic Prompts: Writing and Debugging Simple Code
These prompts are ideal for everyday tasks — generating a function, explaining a snippet, or fixing a small bug.
| Task | Prompt | Example Result |
|---|---|---|
| Write a function | "Write a Python function that takes a list of integers and returns a new list with only the even numbers, sorted in descending order. Include type hints and a docstring." | Returns a complete function with typing imports, a docstring describing parameters and return value, and a one-liner using list comprehension and sorted(). |
| Debug an error | "I have this Python code that raises a KeyError when accessing a dictionary. Explain why and fix it. Code: data = {'a': 1}; print(data['b'])" |
Explains that the key 'b' does not exist, suggests using .get() or a try/except block, and shows the corrected code. |
| Refactor a loop | "Refactor this JavaScript loop into a more functional style using map and filter: let result = []; for (let i = 0; i < arr.length; i++) { if (arr[i] > 0) { result.push(arr[i] * 2); } }" |
Produces const result = arr.filter(x => x > 0).map(x => x * 2); with an explanation of why this is more readable and immutable. |
| Explain a regex | "Explain this regex step by step: ^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$" |
Breaks down each part: start anchor, character class for username, literal @, domain name pattern, dot, and top-level domain with length constraint. |
Example in practice: A junior developer on my team used the 'write a function' prompt to generate a data validation routine for an ETL pipeline. Instead of spending 20 minutes searching documentation, they got a working function in 30 seconds, which they then reviewed and integrated after adding unit tests.
Advanced Prompts: Complex Debugging and Refactoring
These prompts assume you have a multi-file project or a tricky bug that involves understanding context, performance, or design patterns.
| Task | Prompt | Example Result |
|---|---|---|
| Debug a race condition | "I have a Python multi-threaded application that sometimes produces inconsistent output. Here is the relevant code (list of files and key lines). Identify the race condition and suggest a fix using threading.Lock or queue.Queue. Assume Python 3.11+." |
Provides a concrete analysis: two threads modify a shared list without synchronization. Suggests wrapping writes in a Lock context manager and shows the exact lines to change. |
| Refactor legacy code | "Refactor this 500-line PHP function into smaller, testable functions. The function handles order processing: validation, payment, and notification. Suggest a class structure with SOLID principles. Keep the original business logic intact." | Outputs a refactored design with three classes (OrderValidator, PaymentProcessor, NotificationService), each with single responsibility, and a OrderFacade that coordinates them. |
| Optimize SQL query | "This SQL query runs slowly on a table with 5 million rows. Explain why and rewrite it: SELECT * FROM orders WHERE YEAR(created_at) = 2025 AND status = 'pending'" |
Points out that YEAR() prevents index usage. Suggests WHERE created_at >= '2025-01-01' AND created_at < '2026-01-01' AND status = 'pending' and adding a composite index on (status, created_at). |
| Generate unit tests | "Generate pytest unit tests for this Python class that calculates discounts. Include edge cases: negative price, zero quantity, maximum discount cap. Code: class DiscountCalculator: def calculate(price, quantity, discount_percent): ..." |
Produces 8 test cases using @pytest.mark.parametrize, covering normal cases, boundary values, and exception handling (e.g., ValueError for negative price). |
Example in practice: A colleague debugging a Node.js microservice used the race condition prompt. Within one conversation, GPT-4 identified that two async handlers were writing to the same Redis key without atomic operations, and suggested using WATCH/MULTI transactions. The fix reduced data corruption incidents by nearly 100%.
Expert Prompts: Architecture, Security, and Design
These prompts require deep understanding of system design, security patterns, and trade-offs. They are meant for senior engineers or architects.
| Task | Prompt | Example Result |
|---|---|---|
| Design a microservice | "Design a microservice for a notification system that supports email, SMS, and push. Use an event-driven architecture with a message broker (RabbitMQ). Provide a high-level diagram (in text), API contracts, and data model. Discuss trade-offs between consistency and availability." | Returns a detailed design: NotificationService publishes events to RabbitMQ, separate workers consume and send via providers (SendGrid, Twilio, Firebase). API uses a POST /notifications endpoint with a JSON body. Data model includes a Notification table with status, type, and retry count. Discusses eventual consistency and the need for idempotency keys. |
| Security audit prompt | "Review this Python Flask API for common security vulnerabilities. Focus on SQL injection, XSS, and improper authentication. Code: @app.route('/user/<id>') def get_user(id): query = f'SELECT * FROM users WHERE id = {id}'" |
Flags the SQL injection immediately. Recommends using parameterized queries (cursor.execute('SELECT * FROM users WHERE id = ?', (id,))). Also checks for missing input validation and suggests adding a CSRF token for state-changing endpoints. |
| Generate API documentation | "Generate an OpenAPI 3.0 specification for a RESTful API that manages a library of books. Include endpoints for CRUD operations, search by title/author, and pagination. Use proper response codes and error schemas." | Produces a valid YAML/JSON OpenAPI spec with paths like /books, /books/{id}, and /books/search. Includes parameters for query strings, responses with 200, 404, 400, and a reusable Error schema. |
| Code review via prompt | "Act as a senior code reviewer. Review this pull request diff (provide diff). Check for code style, potential bugs, performance issues, and security flaws. Give a rating from 1 (reject) to 5 (approve with minor comments)." | Returns a structured review: three issues found (e.g., missing input sanitization, a potential N+1 query, inconsistent naming). Rating: 3/5. Suggests concrete changes for each issue. |
Example in practice: For a client migrating from a monolith to microservices, I used the 'design a microservice' prompt to draft the architecture for their inventory system. The generated design included a clear bounded context, event schema, and fallback logic. After a two-hour refinement session with the team, we had a spec that saved roughly two weeks of initial design work.
Best Practices for Crafting Your Own Prompts
Based on experience and official guidelines from OpenAI, here are five principles to apply:
- Be specific about the output format — ask for a table, a list, or a code block. Use phrases like "Provide the answer in Markdown with code examples."
- Provide context — include relevant code snippets, error messages, or constraints. The more context, the less the model has to guess.
- Use chain-of-thought — ask the model to "think step by step" or "explain your reasoning" for complex problems. This reduces hallucination.
- Set a role — start with "You are a senior Python developer with 10 years of experience" to bias the response toward expert-level advice.
- Iterate — treat the first response as a draft. Ask for refinements: "Now make this function async" or "Optimize for memory usage."
Conclusion
Prompt engineering is not a mysterious art — it is a systematic skill that improves with practice and structure. The 15 prompts in this article cover a spectrum from writing a single function to designing an entire system. By adapting these templates to your own projects, you can turn GPT-4 from a generic chatbot into a focused development assistant. Start with the basic prompts for daily tasks, then move to advanced and expert ones as you build confidence. Remember: the goal is not to replace your judgment, but to amplify it.
If you work with APIs and want to automate interactions with services like OpenAI, Stripe, or Twilio, consider a platform that simplifies integration. ASI Biont supports connecting to these services through its course and automation tools — explore how at asibiont.com/courses.
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