As a software engineer, embracing AI tools has significantly transformed the way I work, allowing me to learn faster, write cleaner code, and tackle more ambitious projects. These tools act as an intelligent assistant, helping bridge gaps in knowledge and boosting productivity across various aspects of development.

Learning a New Language or Framework

Whenever I dive into a new language or framework, I prefer to work directly with code rather than starting with tutorials. AI makes this approach effortless by generating working code for specific tasks. Rather than spending hours going through documentation or dissecting someone else basic Todo application, I can generate code tailored to my specific learning objectives, for instance creating an application specific to idea or project I am working on. This approach allows me to experiment with real world scenarios that align with my goals, making the learning process both practical and engaging while deepening my understanding of the language in a meaningful way. The core syntax in most languages often looks familiar, generating meaningful code allows me to quickly grasp the structure and then get into the areas I don’t immediately understand from the generated code. It’s like having an expert provide a functional blueprint, letting me focus on understanding the “why” and “how” without getting dragged down in initial setup.

Bridging Knowledge Gaps to Take on Ambitious Projects

AI allows engineers to be more ambitious by compensating for areas where expertise is lacking. Previously, a lack of deep expertise in a specific techology or domain might have stopped me from pursuing certain ideas or working with certain technology. Now, AI can effectively close that knowledge gap. I can be far more ambitious, leveraging AI to assist in areas where I am not a subject matter expert, whether it’s generating boilerplate code for an unfamiliar framework or providing insights into a complex algorithm. It empowers me to explore new territories and develop more innovative solutions.

Simplifying Unit Test Generation

Writing unit tests has never been easier thanks to AI assistants. I can point an AI agent to a specific code file and ask it to generate unit tests, and it does so with great accuracy. This isn’t just about saving time, also it’s about building more robust and reliable software. What’s more, when I use AI to generate new code, I can immediately ask it to generate corresponding tests. This not only helps me understand the AI generated code better but also provides an instant verification that it functions as expected.

Streamlining Code Refactoring

Maintaining a clean and modular codebase through refactoring is essential. AI tools significantly simplify this. If duplicate code needs to be extracted into a separate function, AI can create the new function and intelligently update all relevant call sites. Similarly, the creation of base classes to abstract common functionalities from existing classes becomes a straightforward request, leading to cleaner, more maintainable code with minimal manual effort.

Generating Code from Text Inputs

Imagine having a table in a requirement document detailing properties, example values, and whether they are optional or mandatory. I can simply copy that table and ask AI to create a class with the appropriate properties and attributes, reflecting the optionality or mandatory nature of each field. This accelerates the initial coding phase, reducing the potential for human error and ensuring that the code accurately reflects the requirements from the outset.

Enhancing Debugging Efficiency

The dreaded “compile error” or runtime bug can derail an entire day. AI has become an invaluable debugging assistant. In tools like Visual Studio, if I encounter a compile error, I can ask Copilot to suggest a fix. It not only provides the recommended solution but also offers a clear explanation for the error, enhancing my understanding and allowing me to resolve issues much more quickly.

Expedited Learning Through Examples

For complex code snippets, AI can provide an “example run” detailing variable values at each step (e.g. within a loop), offering invaluable insight into code flow and logic.

Automated Documentation Generation

Generating comprehensive and uptodate code documentation is often a tedious, yet crucial, task. AI largely automates this. I can direct AI to generate comments or full documentation based on code structure and functionality. For instance, it can readily create Swagger UI comments (e.g. summary, description, parameter, and response codes) directly from the code, ensuring API documentation is thorough and accurate without extensive manual effort. This ensures well documented code, enhancing maintainability and knowledge transfer.