Curated by: Sergio A. Martínez
It’s no secret that software development is a rapidly evolving field. Every year, new tools and technologies are being introduced that promise to make developers more productive, and recently, AI has emerged as one of the most promising for software development. We have talked before about how unlikely (at least for now) that AI programs and tools could completely replace a software developer, but that doesn’t mean that these applications can’t have a profound impact on the way we develop software.
In fact, alongside no-code platforms, AI is starting to change the way we approach development from the ground up, requiring new skills to make the most of these technologies. AI-powered tools can help developers write code faster and more accurately, and AI-based software testing and debugging tools can help to find and fix software defects more quickly. So, as this technology continues to improve, more AI-based tools will likely be adopted by software developers. However, how will this profession need to evolve in order to adapt to the AI revolution? What skills will be needed, and what repercussions can AI have in the long term?
Prompt engineering: A new skill to master.
As AI continues to become more advanced and available for the average developer, ‘prompt engineering’ will also become a basic skill for the future programmer.
But what is prompt engineering? Simply put, it’s the process of designing and creating prompts that can elicit the desired response from an AI system. For example, if you are planning a chatbot intended to provide customer support, you would need to create prompts that would help the chatbot to understand the customer’s issue and provide an appropriate response. While AI systems are becoming increasingly capable of understanding natural language, they still rely on prompts to some extent.
“In a way, prompt design is like playing a game of charades”, explains the essay “Prompt Engineering: The career of the future?” by Shubham Saboo. “We give the person just enough information to figure out the word using his/her intelligence. Similarly, with [the language prediction model] GPT-3, we give it just enough context in the form of a training prompt to figure out the patterns and perform the given task.”
As a result, prompt engineering is an essential part of using AI applications, and anybody looking to work with these tools must master them to use their full capabilities in the most efficient way possible. In fact, this skill is becoming more common in all kinds of creative areas, from software to art, where AI applications are getting popular. Jasper.ai, for example, can generate full written content with the correct prompt, and even tools like DALL-E 2 can generate just about any image using the right combination of words. And when it comes to software development, the tools necessary to make this process faster and more efficient are becoming reality.
The machine is my Copilot
“Codex is a family of AI models from Open AI that translates between natural language and code in more than a dozen programming languages”, says the Microsoft GitHub page on prompt engineering. “The power of these AI models is that you can quickly develop and iterate on your ideas and build products that help people do more.”
Ultimately, AI applications like Codex could drastically improve the efficiency of software development, making it possible to create better software in less time. If you’re a developer, chances are you already know about GitHub because it provides an easy way to share and collaborate on code. But what you may not know is that GitHub also offers a tool called Copilot, based on Codex, which can be a big help when it comes to writing better code. Copilot is a tool designed to create code suggestions from plain English prompts (for example: “Write a function that adds two numbers and returns the result”), giving you a hand in solving complex coding puzzles, showing you what the code could look like, or what you might need to make it work as intended. It also includes features like code snippets and templates, which can save you time when working on projects. Oege de Moor, Vice President of GitHub, explains Copilot the best:
“Because it was trained both on source code and natural language, you can write a comment in English, and then Codex will suggest the code that follows. The technology works similarly to auto-complete in word processing but writing code instead of plain language and completing whole functions at a time. When you type in your editor, an excerpt of your code is sent to the AI, which response with ways to complete what you’re typing.”
This could also work as a great teaching tool for people starting to learn to code, streamlining the process and letting its users’ experiment and prototype in a particular language, giving them the chance to test and start over in a very efficient way. Also, it works as a great alternative to no-code platforms where you can hit a ceiling in terms of what you can create, letting a user devise a specific solution by engineering the correct prompt to obtain it.
However, there’s still a long road ahead before Codex, Copilot, or other similar tools become a normal part of software development. For starters, there’s the issue of copyright, a problem shared with DALL-E and other art-generated tools, which have to feed from already created content to generate a result. In the case of Copilot, this means that the AI has been trained on GPL code stored in its servers, which is proven controversial, especially in Open-Source circles that point out that their code can easily be laundered with AI for commercial applications.
With issues like this, it’s important also to start thinking about the discipline and good practices that these tools will require to actually add up to software development. From where we stand today, AI tools are here to stay, and learning to use them the correct way, or understanding the ramifications they will bring to our industry (reportedly, GitHub is working on an “origin tracker tool” to detect copyright violations, for example), there’s no doubt that the next few years of software development will change radically.
With an AI companion at your side, whether you’re just getting started with coding or you’re an experienced developer, tools like GitHub Copilot can be a valuable resource for improving your productivity and writing better code. With that in mind, what will you be able to achieve tomorrow?
The Key Takeaways
- AI tools are here to stay, and it’s important to be ahead of the curve and see the opportunity they bring in software development.
- One of the most important skills to develop is ‘prompt engineering’: the ability to tell a machine exactly what you need to get the best result possible.
- Software development is already changing thanks to AI-based coding tools like Codex and Copilot, which promise to make the process of development faster and more efficient.
- Discipline and good practices are going to become a necessity to use these tools to their full potential while due diligence and copyright considerations will become a ‘must have’.
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