Can Chatgpt Write Code? How To Use It Well?

This article explores whether ChatGPT can write code and provides a detailed guide on how to leverage ChatGPT for coding projects.

Key Takeaways

• ChatGPT can write and debug code across various languages, generate boilerplate code, and assist in error resolution.
• Despite its advanced capabilities, ChatGPT requires human supervision to ensure code quality and effectiveness.

Can ChatGPT Write Code?

Sure, ChatGPT can write code. ChatGPT can write parts of code or even more detailed code in many computer languages, like Python, JavaScript, C#, and Java. It can do simple tasks, like making a basic “Hello World” web page, or more complicated things like solving problems with data or making tests for checking code.

For example, if you need a webpage that just says “Hello World,” ChatGPT can make the code for that really fast. This is good because it lets people who make software spend less time on easy stuff and more time on harder work.

Even though ChatGPT can make code, it’s still important for the people making software to check the code. They need to make sure it does what they want, follows the rules for good code, and works well. This means ChatGPT helps people who write code by doing some of the work, but it doesn’t take their place.

Read more: An Ultimate Guide To Use ChatGPT In Python

How to Use ChatGPT to Write Code

Getting started with using ChatGPT for coding in your projects is quite straightforward. Here’s a simplified step by step guide to help you begin:

1. Sign Up: Go to OpenAI’s ChatGPT website. You’ll need to sign up, confirm your email address, and verify your mobile number to get started.

2. Start a Session: Once logged in, look for the option to start a new conversation or use a specific feature, and select Q&A if available.

3. Type Your Query: Enter your coding request with as much detail as possible. Remember to specify the programming language and the task you need help with.

4. Submit and Await Response: After typing your inquiry, send it. Then, simply wait a moment for ChatGPT to generate a response, which will appear on the left side of the chat window.

5. Implement and Test: Once you receive the code, incorporate it into your project. It’s essential to test the code to ensure it meets your requirements and works as expected.

6 Strategies for Using ChatGPT for Writing Code

1. Be Specific with Your Requests: The more detailed your instruction, the more accurate the code ChatGPT generates. Include specifics about the programming language, the objective, and any particular requirements or constraints.

Choosing a Programming Language:

• Python: Ideal for beginners due to its clear syntax and readability, perfect for a wide range of projects, from web development to data science.
• Node.js: Excellent for creating scalable network applications, offering robust solutions for backend development.
• CSS and HTML: Fundamental for web developers aiming to design engaging and structurally sound websites.

2. Format Your Prompt Properly: Ensuring your prompt is clearly and correctly formatted helps ChatGPT better comprehend your request. This includes using proper capitalization, punctuation, and structure tailored to your programming language of choice.

3. Provide Examples: Sharing examples of the expected outcome or similar code snippets enhances ChatGPT’s ability to tailor its output to your needs, offering a clearer insight into your objectives.

4. Define the Scope Clearly: It’s crucial to clarify the exact scope of the code you’re seeking. If it’s a function, specify the expected inputs and outputs.

5. Context Matters: Describing the environment in which the code will operate, including its relationship with other components and its role within the overall project, allows ChatGPT to generate code that fits better within your project’s architecture.

6. Experiment with Different Prompts: Since ChatGPT learns from the prompts given, experimenting with various instructions and comparing results can help pinpoint the most effective way to communicate your needs.

Examples of Codes Written by ChatGPT

1. To Create New Code

Context and Purpose: Suppose you’re learning to program and want to practice by creating a Python function. Your goal is to write a simple function that adds two numbers and returns the result. This exercise helps understand functions and basic arithmetic operations in Python.

Prompt: “Write a Python function that adds two numbers.”

Example Code:

2. For Specific Tech Stacks

Context and Purpose: You’re a beginner web developer familiarizing yourself with HTML and CSS code. Your task is to create a simple webpage that displays “Hello, World!” in red color. This basic exercise helps understand how HTML structures content and how CSS styles it.

Prompt: “Create an HTML file with a paragraph styled with CSS to display ‘Hello, World!’ in red.”

Example Code:

3. To Debug Code

Context and Purpose: Imagine you’re learning JavaScript and encountered an error with your code that’s supposed to alert “Hello, World!” but doesn’t work. The task is to identify and fix the issue, helping you practice debugging skills.

Prompt: “The following JavaScript code should display an alert with ‘Hello, World!’ but it doesn’t work. Can you fix it?”

Original Code (with an error):

Fixed Code:

4. To Evaluate Technologies

Context and Purpose: You’re curious about data storage options for your new Python project and are considering lists and dictionaries. You want to understand which is more efficient for storing and accessing data by key.

Prompt: “Write Python code to demonstrate storing and accessing data using a list and a dictionary, highlighting the difference.”

Example Code:

This code illustrates the basic difference in data access between lists (by index) and dictionaries (by key), guiding beginners on choosing the right data structure based on their needs.

What Types Of Coding Can ChatGPT Do Well?

Here’s an overview of the types of coding ChatGPT can handle effectively:

1. Boilerplate Code Generation: ChatGPT can swiftly generate boilerplate code for numerous programming languages and frameworks, helping developers jumpstart their projects by reducing the time spent on repetitive coding tasks.

2. Web Development: It is adept at crafting code for both front-end and back-end web development. This includes HTML, CSS, JavaScript for front-end work, and server-side languages like Python (with Flask or Django), Node.js, Ruby on Rails, and PHP for back-end development.

3. Data Analysis and Science: ChatGPT can write Python code involving data manipulation and analysis using libraries like Pandas and NumPy, as well as visualization libraries such as Matplotlib and Seaborn. It can also generate SQL queries for database management and data retrieval.

4. Machine Learning and AI: It can generate code snippets for machine learning models using libraries like scikit-learn, TensorFlow, and PyTorch, assisting in data preprocessing, model building, training, and evaluation.

5. APIs and Automation Scripts: ChatGPT can help in writing scripts for automating tasks, such as file operations, data scraping, and API interactions, using Python or shell scripts.

6. Educational Purposes and Learning: It serves as an excellent tool for educational purposes, providing examples and explanations of coding concepts, algorithms, and data structures in various programming languages, making it a valuable resource for learners.

7. Debugging and Code Explanation: ChatGPT can offer suggestions for debugging code and provide explanations for existing code snippets, helping developers understand and fix issues in their code.

8. Test Cases and Documentation: It can help write test cases for software testing in frameworks like JUnit for Java, pytest for Python, and others. Additionally, ChatGPT can assist in generating documentation for code, improving code readability and maintainability.

Limitations in ChatGPT’s Ability To Write Code

• Understanding Context and Requirements: ChatGPT may struggle with interpreting complex or vague project requirements. Precise, clear, and detailed prompts are necessary to guide it towards generating useful code snippets.
• Keeping Up with Latest Technologies: Given its training data cut-off in April 2023, ChatGPT might not be up-to-date with the latest programming languages features, frameworks, libraries, or best practices that have evolved or been introduced after that date.
• Debugging Complex Issues: While ChatGPT can assist with common debugging tasks, its ability to diagnose and fix complex or deeply rooted issues in code can be limited, especially when such problems require domain-specific knowledge or an understanding of the intricate details of a project’s architecture.
• Performance Optimization: The code generated by ChatGPT may not always be optimized for performance. It might provide a solution that works but could be inefficient or not the best practice, especially in cases where optimization is critical.
• Security Considerations: ChatGPT might not always adhere to security best practices in its code output. Developers should be vigilant about security implications and vulnerabilities in the generated code, particularly for web development and software dealing with sensitive data.
• Understanding the Entire Project Scope: Generating code that fits seamlessly into an existing project’s architecture or ecosystem can be challenging for ChatGPT. It may not fully grasp the project’s scope or the interactions between various components, potentially leading to integration issues.
• Custom and Advanced Solutions: For highly specialized or cutting-edge tasks, the solutions provided by ChatGPT may be too generic or not advanced enough, requiring significant modification to meet specific project needs.
• Code Reviews and Quality Assurance: The code generated by ChatGPT should undergo thorough review and testing by human developers to ensure it meets quality standards, functions correctly within the application, and doesn’t introduce bugs or vulnerabilities.
• Interpreting Error Messages and Logs: While ChatGPT can offer suggestions for common errors, interpreting and solving issues from cryptic error messages or logs, especially those specific to a certain tech stack or project, can be outside its capabilities.
• Emulating Human Intuition and Experience: Certain aspects of programming require human intuition, creativity, and years of experience, which ChatGPT, as an AI tool, cannot replicate. This includes making nuanced design decisions, architecting complex systems, and foreseeing potential issues based on past experiences.

People also read: How to use the ChatGPT Code Interpreter Feature Effectively

5 Best AI Tools For Software Developers in 2024

ChatGPT

ChatGPT is an advanced AI language model developed by OpenAI, capable of understanding and generating human-like text. It assists with a variety of tasks, from writing code to casual conversation, leveraging vast knowledge in programming languages, natural language processing, and more.

Key Features:

• Code Generation: Generates code for a variety of use cases, adapting to different programming languages and styles.
• Code Explanation and Debugging: Analyzes and explains code, offering solutions to identified problems and bugs.
• Documentation Writing: Automatically creates comprehensive documentation for your codebase, saving time and effort.

Pricing: Offers a free version; paid versions start at $20/user/month with additional features.

Pros: Has a supportive community and receives regular updates.

Cons: May produce occasional inaccuracies and involves initial setup costs.

GitHub Copilot

GitHub Copilot is an AI-powered code completion tool that suggests whole lines or blocks of code as you type, working alongside developers directly in their editors, like Visual Studio Code, to enhance their coding efficiency across numerous programming languages.

Key Features: 

• Code Autocompletion: Suggests complete lines or blocks of code, speeding up the development process.
• Multi-language Support: Works with numerous programming languages, making it versatile for different coding projects.
• Contextual Understanding: Understands the context of the code to provide relevant suggestions, improving coding efficiency.

Pricing: Personal plans at $10/month; business plans start at $19/user/month.

Pros: Enhances coding speed and offers integration with existing tools.

Cons: Results require manual review and there are licensing considerations.

Otter.ai

Otter.ai is a voice-recognition platform that provides real-time transcription services, converting live speech and recordings into searchable, shareable text, facilitating note-taking and documentation for meetings, interviews, and lectures.

Key Features: 

• Real-time Transcription: Transcribes conversations with high accuracy in real-time, ideal for meeting records and notes.
• Integration with Video Conferencing: Syncs seamlessly with popular video conferencing tools to capture every word.
• Shared Notes and Collaboration: Offers shared access to meeting transcriptions and summaries, enhancing team collaboration.

Pricing: Free basic plan available; paid plans start at $16.99/user/month.

Pros: Boosts productivity and is user-friendly.

Cons: Requires internet and may have difficulty with noisy backgrounds.

Snyk

Snyk is a developer-first security platform that integrates with developers’ existing workflows to detect and automatically fix vulnerabilities in their code, dependencies, containers, and infrastructure as code, aiming to make software development more secure.

Key Features:

• Automated Vulnerability Detection: Continuously scans and identifies code vulnerabilities for immediate resolution.
• Comprehensive Security Solutions: Provides a holistic security scan, including dependencies and infrastructure as code (IaC).
• Direct Integration: Fits into existing development environments, supporting a range of IDEs and repositories.

Pricing: Free for individuals; team plans from $25/month/product.

Pros: Provides continuous monitoring and easy integration.

Cons: Can be complex to set up and may have a confusing user interface.

Tabnine

Tabnine is an AI-driven code completion assistant that supports software developers by providing smart completions for code based on the context of the work, compatible with various IDEs and coding languages, aiming to streamline the coding process.

Key Features: 

• Code Completion: Gives code suggestions that adapt to your personal coding style for enhanced workflow.
• Tabnine Chat: Allows for natural language queries to generate and document code directly within the IDE.
• Security Features: Offers robust security measures for safe coding, with options for on-premises deployment.

Pricing: Basic free plan available; professional plans start at $12/user/month.

Pros: Increases coding efficiency and offers customized suggestions.

Cons: The cost can be a factor, and it lacks refactoring or debugging functionalities.

Future of LLMs – Statistics

The LLMs (Large Language Model) market is experiencing rapid growth, set to jump from $1,590 million in 2023 to $259,818 million by 2030. This growth is fueled by advancements in models like LLaMA, Bloom, and GPT-3.5, improving their speed and accuracy for industry applications.

It has been demonstrated that LLMs have made significant progress. Because of these enhancements, not only have they become quicker and more accurate, but they have also significantly expanded their utility in automating and improving processes across a variety of industries.

In the fields of e-commerce, education, finance, and healthcare, LLMs are increasingly being used for functions such as customer service and diagnostics. This is despite the fact that there are challenges associated with accuracy for particular tasks. The impact that they have is significant, ranging from the simplification of customer service to the facilitation of diagnostics and the automation of grading.

Nevertheless, there is room for more widespread integration, despite the fact that different businesses have different adoption rates and there are concerns regarding privacy and ethical use. Significant obstacles continue to stand in the way of widespread integration into business operations, including concerns regarding privacy and ethical considerations.

North America, Europe, the Asia-Pacific region, Latin America, and the Middle East and Africa are all expected to experience significant growth in the market. The expansion of the market is geographically comprehensive. For example, it is anticipated that the LLM market in North America will grow to more than $105 billion by the year 2030. This growth is a reflection of the increasing integration of the technology into various aspects of the economy and society.

The leadership position in the LLM space is held by key players such as Open AI (ChatGPT), Google (PaLM), Meta (LLaMA), and others. These key players collectively accounted for approximately 88.22% of the total revenue in the world in 2023. These companies have made significant investments and made significant breakthroughs, which have driven forward the capabilities and applications of LLMs. This concentration of market power among top vendors is a testament to these companies’ achievements.

FAQs: Can Chatgpt Write Code

Conclusion

In conclusion, ChatGPT’s abilities in code generation have significant implications for the future of software development. While it is not a replacement for human expertise, its value as a tool that can speed up development and assist with the more mundane aspects of coding is undeniable.