Coding with ChatGPT: A Two-Week Review of Daily Programming Tasks

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Eduardo Maciel
Senior Software Engineer

As a software developer, I’m always looking for tools and technologies that can make my coding more efficient and productive. When I first came across ChatGPT, I was immediately captivated by its potential application in the realm of software development. Excited to explore its capabilities, I embarked on a two-week experiment, testing ChatGPT with my daily programming tasks. 

As I went, I documented my experience. In this article, I’ll walk you there my discoveries and revelations that unfolded during these weeks of coding with ChatGPT. First, we’ll dive deep into the various functions and capabilities I used, such as code analysis and suggestions, code refactoring, debugging assistance, documentation generation, and code reviewing. There, I’ll showcase real-life code examples from my TypeScript, GraphQL, NestJS, and MongoDB projects. Next, I touch on 4 key takeaways on how it improved my coding experience. Lastly, I’ll finish up with some lessons I learned along the way.

Join me as I share firsthand my coding experience with ChatGPT.

Table Of Contents

My two-week experiment with ChatGPT: Work & Project Context

Integrating ChatGPT in my development tasks with TypeScript and RESTful APIs

As a software developer, I primarily work with TypeScript using frameworks, such as Nest.js to build scalable and maintainable applications. During this experiment, I focused on projects involving RESTful APIs, authentication, and database integration and actively incorporated ChatGPT into various coding tasks by leveraging its capabilities to streamline my workflow. Below is an overview of the specific tasks I performed using ChatGPT.

Setting the Stage: Project Overview and the Technologies Involved

To accurately gauge how useful ChatGPT is to me while I code, it’s important to understand my work context. I code for a web application that uses a tech stack combination of TypeScript, GraphQL, NestJS, and MongoDB. The application provides seamless data management for users and focuses on scalability and performance. 

My role on the project involves developing backend services, designing and implementing GraphQL APIs, and optimizing database interactions. My goal with using ChatGPT as a coding tool is to enhance the quality of my code, improve my productivity, and ultimately deliver a more robust and efficient application.

Originally published on Aug 16, 2023Last updated on Jan 13, 2025

Key Takeaways

Does ChatGPT code actually work?

ChatGPT can generate functional code snippets. However, the code it provides requires careful human review and testing. Mainly, that’s because it operates in a simple prompt-and-reply method, where it must generate the code from a single input from the user. It becomes particularly challenging when coding tasks are complex or involve many steps. Developers should always review AI-generated code to understand its context and validate functionality before implementation.

Is it safe to put code in ChatGPT?

Entering code into ChatGPT is generally safe, provided it’s not proprietary or sensitive. For instance, providing secret tokens or API keys are risky, as is sharing any sensitive or proprietary code. There are a few reasons for this. First, ChatGPT may store your data on its servers, which can become public in the event of a breach. Additionally, your data may be used to train future iterations of the AI models, risking exposure. It may also violate privacy laws, depending on the data provided. Developers should exercise strong caution by avoiding sharing sensitive or proprietary code and understanding the potential exposure of confidential information.

Is there something like ChatGPT for coding?

Yes, there are many tools similar to ChatGPT that are specialized for coding. One of the most popular ones is Github Copilot, which works directly in your code editor and provides relevant suggestions. There are many other tools on the market, including AWS Code Whisperer and OpenAI Codex. These tools provide unique features for code generation, completion, and optimization, giving developers multiple options to enhance their coding workflow and productivity.