Five ways in which large language models can make you a better software developer

The development and widespread use of large language models, such as the constantly mentioned GPT-4 and Bard, have the capabilities to revolutionise a variety of domains, and software development is at the forefront of this. These models, indeed, have the ability to generate coherent and contextually relevant pieces of text (and code) from natural language descriptions (which are called “prompts”), and from now on there will likely be a huge distinction between the developers that are capable or leveraging this technology and the ones that ignore its potential. In this article, we will discuss how the rapid diffusion of these models is reshaping the roles of software engineering and developers, and five ways in which they can make you a better software developer.


1. Faster prototyping and Conceptualization

Traditionally, software engineers and developers spend a considerable amount of time transforming abstract ideas into concrete implementations. With large language models, this process becomes significantly accelerated. By describing a desired feature or functionality in natural language, developers can now generate prototype code snippets or even entire components quickly. This allows for rapid experimentation and concept validation, enabling software teams to iterate and refine their ideas at an unprecedented pace.

Example: A developer can describe a simple web application with user authentication requirements. Using a large language model, they can generate the initial code structure, including user registration, login, and session management. This eliminates the need to start from scratch, saving valuable time and effort.


2. Automated Code Generation

Large language models offer the potential to automate certain code generation tasks, reducing the manual effort required by developers. Instead of painstakingly writing lines of code, software engineers can leverage these models to generate boilerplate or repetitive code snippets automatically. This automation can streamline the development process, allowing developers to focus on higher-level design and problem-solving aspects.

Example: In a machine learning project, a software engineer needs to implement data preprocessing steps such as scaling, normalisation, and feature extraction. Instead of manually writing the code for each step, they can describe the requirements to a large language model, which would generate the necessary preprocessing pipeline, complete with appropriate libraries and configurations. 


3. Enhanced Documentation and Knowledge Transfer

Documenting software projects is a critical but often time-consuming task for developers. Large language models can assist in automating parts of this process by generating documentation from code comments, variable and function names, and contextual information. By analysing the codebase, these models can produce comprehensive and readable documentation, aiding in knowledge transfer and improving project maintainability.

Example: A developer can provide a high-level overview of a complex algorithm or library through natural language descriptions. A large language model can then generate detailed API documentation, including usage examples and parameter descriptions, making it easier for other developers to understand and utilise the functionality.


4. Intelligent Code Assistance and Bug Detection

Software engineers often rely on Integrated Development Environments (IDEs) for code completion, error detection, and refactoring assistance. With the integration of large language models into IDEs, developers can expect more advanced and context-aware code assistance. These models can provide suggestions, catch potential errors, and offer solutions to common programming challenges, further augmenting developer productivity.

Example: While writing code, a developer can rely on a language model-powered IDE that offers real-time suggestions for method calls, code snippets, or refactoring options. The model can even identify potential bugs based on the code context and provide actionable recommendations to rectify the issues.


5. Jumping into new languages and tasks

Software developers usually work with one or few languages for the majority of the time, and might find it challenging to transfer the things they have learnt into new languages. Large language models can help in this as well, since they have been trained on a variety of programming languages and can provide code snippets or “translate” a piece of code from one language to another. In other words, by utilising large language models, developers can quickly access a wealth of information, guidance, and code samples related to their target programming language. These models can help developers overcome initial challenges, accelerate their learning process, and provide them with the necessary knowledge to start working effectively in a new language.


  • A developer who primarily works with Python wants to start learning JavaScript. They can leverage a large language model to quickly obtain information and guidance on JavaScript concepts, syntax, best practices, and common libraries.
  • The case of a developer who wants to develop a small game from scratch.

Some considerations

By leveraging the power of natural language descriptions, software development processes can be accelerated, automated, and made more efficient. From faster prototyping and automated code generation to enhanced documentation and intelligent code assistance, these models unlock new possibilities for increased productivity and innovation in the software engineering field. As these technologies continue to advance, software professionals must adapt and embrace them to stay ahead in an evolving landscape where large language models are becoming invaluable in the software development process. However, it's important to note that while large language models bring about numerous benefits, they also come with certain considerations, and their instructions should not be blindly followed.

To be more precise, any software developer using these tools should always:

  • Look at the generated code and understand it before trusting it and using it;
  • Be aware of the ethical implications of using large language models, and the possible biases that they exhibit;
  • Continue learning and develop their skills to stay up to date with these tools and the software development landscape

In other words, software developers should be AI literate before using these tools. In conclusion, the rapid diffusion of large language models in software engineering presents exciting opportunities to streamline and automate various aspects of the development process. From faster prototyping and automated code generation to enhanced documentation and intelligent code assistance, these models have the potential to significantly improve developer productivity and efficiency. However, careful consideration of their limitations, ethical implications, and ongoing skill development is necessary to harness their benefits effectively. By embracing these advancements, software engineers can navigate the changing landscape and drive innovation in the ever-evolving world of software development.