A Coding Companion: Generative AI for Developers

Disha Prakash
Disha Prakash
20 Feb 20243 mins read
Languages, frameworks, tools, and trends

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Generative AI, a subset of artificial intelligence, is the latest phenomenon reshaping the software development landscape. Beyond creating, developers can now innovate and imagine on an unprecedented level.

According to McKinsey, with generative AI, routine developer tasks, such as code documentation for maintainability, can be accomplished in half the time, new code can be generated nearly twice as fast, and code refactoring can be completed in two-thirds of the time.

While using AI tools makes developers’ lives easier, the time saved is in proportion to the complexity of the task and the developer’s capability. For instance, when not well-versed in the programming framework at hand, a developer may take more time to use the same tool.

Regardless of a developer’s experience level, employing AI tools consistently enhances the quality of code compared to working without them. The optimal use of these tools is to serve as helpful assistants rather than take over entire tasks. The key lies in understanding how to effectively leverage the tool to generate the required output, turning the process into a collaborative effort between the developer and the AI.

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Three crucial areas where generative AI tools enhance productivity

  1. Eliminating manual and repetitive work: Generative AI speeds up your work, automatically filling in standard functions and completing code as you type, freeing up your time for other tasks.
  2. Code suggestions: Generative AI can provide you with helpful code suggestions to get a head start while writing code.
  3. Improvements to existing code: Existing code can be improved faster with effective prompts from generative AI tools.

Why does developer expertise matter in using these tools?

  1. Generative AI operates based on patterns learned from massive amounts of data. Although it can provide valuable assistance, it may not always fully understand the specific nuances or requirements of a particular coding scenario.
  2. The coding requirements of a project are best understood by the developer involved. Improving code involves a need for developer expertise and the ability to make necessary improvisations. This also ensures that the final code doesn’t encounter any integration issues with other applications.

Some generative AI tools for developers

Over the past few years, several generative AI tools have emerged that can seamlessly integrate with the workflow of software developers. These applications make use of large language models (LLMs) to comprehend language, imagery, and code to provide answers, code snippets, and other valuable insights. They’re not flawless, but they can prove beneficial when used with careful oversight and supervision.


OpenAI’s ChatGPT is free to use (the Plus version is paid). You don’t have to install it or provide it with access to your codebase to get suggestions. It can serve as a good starting point for your software development efforts.

Google Bard

Bard is Google’s equivalent of ChatGPT. Google developed transformer architecture especially for Bard. It can provide coding assistance for 20 programming languages. Among other things, it can refactor your code, explain code, or even generate code from your prompts.

GitHub Copilot

Unlike the ChatGPT and Bard which use general-purpose LLMs, GitHub Copilot uses OpenAI Codex trained on vast amounts of code. This specialized design allows Copilot to excel in crafting functional code across languages like Python, Javascript, Go, PHP, Ruby, and Swift. Also, it has full access to your project which makes it well-adept at giving code suggestions based on architecture, style conventions, and context.


Generative AI marks the start of a revolutionary era in software development, enabling developers to achieve tasks at an impressive speed and fostering heightened efficiency and innovation. As it progresses, the potential for improved development quality becomes increasingly evident. However, harnessing the full potential of this technology entails more than just having advanced tools; it requires a thoughtful and comprehensive approach whereby developers upskill themselves, choose the right use cases, and exercise proper risk controls to ensure responsible and effective generative AI deployment.

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Disha Prakash

Disha Prakash

Disha Prakash is a writer with around eleven years of experience writing in diverse domains. Besides, she holds a few research papers in computer vision and image processing published in international publications. In her free time, she loves to read books, do yoga, and meditate.

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