Neural networks have long stopped being just a text-generation tool. Today AI is actively used in software development: it helps write code, find bugs, create tests, explain complex parts of programs, and work with documentation.
Modern AI tools let you automate routine tasks and cut development time. Instead of spending hours on boilerplate code, a developer can focus on application architecture and business logic.
Among the most popular solutions are GitHub Copilot and ChatGPT. Despite a similar goal, these tools are used differently and complement each other well.
What an AI coding assistant is
An AI coding assistant is an AI-powered development helper that analyzes project context and suggests ready-made solutions right as you write code.
Such tools can:
- finish writing functions;
- generate boilerplate code;
- create tests;
- find bugs;
- explain how specific parts of a program work;
- help with documentation.
Essentially, AI takes over a significant share of repetitive tasks, letting the developer work faster.
How AI generates code
Autocomplete
One of the most common scenarios is intelligent code continuation.
A developer only needs to start writing a function or leave a comment, and the neural network suggests a ready implementation.
For example, the comment:
# function to sort a list of users by age
can automatically turn into a complete function.
This approach is especially useful when writing typical code that regularly repeats across different projects.
Generating functions from a description
Modern models can produce working code from a plain text request.
For example:
Create an API for fetching a list of products with FastAPI.
After that, AI can propose a project structure, routes, request handlers, and basic business logic.
This significantly speeds up building prototypes and testing new ideas.
Generating tests
Testing often takes no less time than developing the functionality itself.
AI can analyze existing code and automatically create tests, which then just need to be reviewed and refined if necessary.
GitHub Copilot for everyday development
GitHub Copilot works directly inside the IDE and suggests code options as you write a program.
It helps:
- create functions;
- write SQL queries;
- generate tests;
- work with APIs;
- create documentation;
- quickly write repetitive code.
Copilot is most useful for routine tasks that require a large amount of similar, repetitive constructs.
ChatGPT for programming
How it differs from Copilot
Unlike GitHub Copilot, ChatGPT works in a conversational format.
A developer can ask the model to:
- explain an error;
- find a problem in the code;
- suggest a more efficient implementation;
- write a function from a description;
- explain how a library or framework works;
- compare several solution options.
This format is especially convenient when it's important to understand the causes of a problem, not just get ready-made code.
Error analysis and refactoring
One of the most useful applications of ChatGPT is analyzing an existing project.
AI can:
- find potential bugs;
- improve the structure of functions;
- suggest clearer variable names;
- recommend optimization approaches;
- explain complex algorithms.
That said, any changes need to be tested before being used in a production project.
Practical examples of using AI
Building a REST API
Say you need to quickly build an API for fetching a list of products.
It's enough to phrase a request like:
Create a FastAPI API for fetching a list of products with the fields id, name, and price.
After that, AI will propose a working service foundation, which you just need to adapt to your specific project.
This approach significantly speeds up development of prototypes and internal services.
Working with documentation
Another popular scenario is preparing documentation.
AI can automatically create:
- function descriptions;
- code comments;
- request examples;
- API usage instructions.
This is especially useful in large projects where documentation is constantly updated.
Benefits of using AI in development
Using neural networks provides several practical benefits:
- speeds up writing typical code;
- helps learn new technologies faster;
- automatically creates tests;
- helps find bugs;
- simplifies documentation preparation;
- reduces the amount of routine work.
As a result, developers can spend more time on application architecture, design, and solving complex engineering problems.
Limitations and risks
Despite the high quality of modern models, you can't fully rely on AI.
A neural network can:
- use outdated approaches;
- suggest methods that don't actually exist;
- generate inefficient code;
- make logical errors in a program.
You also need to consider security. Generated code may contain potential vulnerabilities or fail to account for the specifics of a particular infrastructure.
It's equally important to follow confidentiality requirements. If a project contains a company's internal code, you should factor in corporate policies and the rules for using cloud AI services.
Where to use different AI models
In practice, many developers use several models at once.
Some are better suited for generating code, others for finding bugs, and others for architecture analysis or documentation preparation.
To avoid switching between different services, it's convenient to use platforms that combine several models in one interface. For example, GPTunneL, which offers various AI tools for programming, text generation, and other work tasks.
Conclusion
AI has already become a full-fledged tool for modern developers. It helps write code faster, create tests, find bugs, and prepare documentation, cutting the time spent on routine tasks.
At the same time, AI doesn't replace a programmer. Any generated code needs to be reviewed, tested, and adapted to the requirements of a specific project.
Used as an assistant rather than a replacement for a developer, AI can genuinely speed up the development process and boost a team's productivity.