4 ways devs can use ChatGPT to be more productive

ChatGPT crafted this limerick that effectively captures how artificial intelligence (AI) and machine learning (ML) in 2023 can boost developer productivity.

Ever since OpenAI launched the new application ChatGPT, people around the world have been using it for all kinds of tasks — from generating code snippets and essays to conducting research. Users have even explored what a poem written by their favorite fictional character would look like, according to ChatGPT. But today, we're going to explore how it can assist developers in writing better code at a faster clip.

The AI research laboratory behind ChatGPT, OpenAI, states that its mission is to create AI that "benefits all of humanity." We're still waiting to see whether OpenAI will fulfill its ambitions, but there's no question that ChatGPT has sparked the interest of professional software developers.
From analyzing and debugging code to generating code based on problem statements, ChatGPT has already begun to help with various common development use cases. iOS developer Felix Krause explains one such case:

Artificial Intelligence and chatbots, like ChatGPT, have already changed the software development landscape. As a platform that advocates for developer learning and upskilling, we firmly believe ChatGPT can and will help us make better software. Text generation can't replace developer jobs, but it sure can help developers up their output in these careers.

In this article, we’ll explain four ways in which ChatGPT can maximize your productivity as a developer, as well as four reasons why it is not an -be-all and end-all solution to writing code. Using ChatGPT to its fullest potential while avoiding its pitfalls will allow your productivity to skyrocket!

We’ll cover:

How ChatGPT can help devs write better code
4 areas where ChatGPT falls short
Start leveraging AI for yourself

How ChatGPT can help devs write better code

ChatGPT is not the first machine learning tool to serve as a coding assistant.

Autocomplete and text-generation software has been helping us type code and even email faster for several years. We also have GitHub Copilot, which uses a production version of OpenAI's GPT-3 to suggest improvements and flag potential problems in our code. As a coding assistant, ChatGPT distinguishes itself from Copilot with the ability to formulate detailed responses to conversational prompts instead of basic pre-programmed commands.

Here are four distinct ways using ChatGPT can make your life as a developer simpler.

1. Making coding more accessible

Throughout the history of computer science, we've seen technological advancements that have enabled many more people to become developers. Largely thanks to methods of abstraction, it has become easier for more and more people to leverage complex technologies once only understood by highly specialized engineers.

For instance, high-level programming languages, in tandem with compilers and IDEs, allow today’s engineers to write human-readable code without having to write machine code (which is in binary and not human-friendly). Similarly, the improvement of AI assistants like Copilot is a promising sign that we're still moving toward making coding a more accessible and enjoyable experience for all.

Benefitting from abstraction doesn't necessarily mean that developers would be any less skilled or knowledgeable. Similarly, not knowing how a car engine works doesn’t make you a bad driver, and using autocomplete doesn't make you a bad engineer. We can still build beautiful applications while benefiting from high-level languages like Java or machine learning tools like ChatGPT.

2. ChatGPT as a research assistant

ChatGPT has been trained on over 45 terabytes of text data from various sources, including CommonCrawl, WebText2, and code in Python, HTML, JavaScript, and CSS.

ChatGPT generates responses based on this vast training dataset — and conveniently does so in response to human input. The ability to interpret human input can make ChatGPT a helpful research assistant. While its results still need validation, it can provide accurate results that can save us from scouring search engine results or StackOverflow. It can even offer further explanations that will aid coders in learning and understanding new concepts.

This benefit can help us streamline our search for new and relevant knowledge while coding. No developer knows everything, and questions are bound to pop up in your mind every now and then. Hopping over to the OpenAI tab and having ChatGPT answer questions can save a lot of time spent researching. You shouldn't use ChatGPT to pull all of your information, but this is an excellent method to get an answer in a matter of seconds.

3. Reducing tedium with ChatGPT

ChatGPT will make coding more productive and bug-free. As it accommodates more complex requirements, we can look forward to it helping eliminate grunt work and accelerate productivity and testing.

As assistants such as ChatGPT evolve, many of the tedious tasks that have occupied developers could go away in the next decade, including:

Automating unit tests
Generating test cases based on parameters
Analyzing code to suggest security best practices
Automating QA

Another major benefit is automating the mundane task of creating documentation. ChatGPT can help developers generate documentation for their code, such as API and technical documentation. For example, ChatGPT can analyze your code and extract valuable information such as function and variable names, descriptions, and usage examples. It can then use this information to create detailed reports that are easy to navigate. This automation can save developer teams significant time and effort that would otherwise be dedicated to manually constructing the necessary documentation.

Other forms of documentation, such as user manuals, release notes, troubleshooting guides, and more, can also be expedited by ChatGPT. Although this chatbot cannot be a replacement for understanding your code, it is a great tool for efficiently maintaining proper documentation so that other teams (and new team members) can easily understand the dev team's workflow.

Relieving developers of menial tasks can free them to think about more complex issues, optimizations, and higher-level concerns, such as an application's implications for its users or business. Allowing this new AI chatbot to perform these actions, such as data processing, will open up your work schedule to focus on more critical and creative projects.

Some people think assistant tools make developers lazy. We strongly disagree. Once you've learned something, there's no cognitive or productivity benefit to retyping the same line of code repeatedly. Why reinvent the wheel if the best code for a particular task has already been tried and tested? Besides, the problem you're solving is probably more complicated than copying/pasting a few code snippets.

This benefit is analogous to how APIs simplified devs' lives. For instance, the payment processing that Stripe now hides behind a single API once required developers to write 1,000 lines of code.

4. Using ChatGPT for Natural Language Processing

Natural language processing (NLP) is a subset of machine learning that uses software to manipulate and produce natural languages, such as the text that appears when you ask ChatGPT a question or the speech you hear from an AI bot like Alexa or Siri. Tasks such as translating between languages, text analysis, speech recognition, and automatic text generation all fall under the umbrella of natural language processing.

Here are a few examples of how ChatGPT can aid developers with natural language processing.

Sentence parsing: ChatGPT can parse natural language inputs and extract the desired information, such as entities and actions. This information can be used to identify the necessary requirements.
Text classification: ChatGPT can classify natural language inputs into predefined categories such as functional requirements, non-functional requirements, or constraints.
Summarization: ChatGPT can summarize natural language inputs into a more concise and actionable form, which can help developers quickly understand the key requirements.
Dialogue-based: ChatGPT can assist in a dialogue-based approach, where developers can ask follow-up questions to gather more clarification on the requirements.

Using natural language processing techniques, ChatGPT can help devs gauge the requirements expressed in natural language. They can then transform this information into actionable requirements to guide development.

It's important to note that these examples pertain to one natural language processing use case. Your approach will depend on the context and conditions of your project.

4 areas where ChatGPT falls short

ChatGPT is not magic. It looks at a massive corpus of data to generate what it considers the best responses based on existing code.

Accordingly, it definitely has its limitations. Be wary of these limitations while utilizing ChatGPT to your benefit.

1. Human judgment is still required

ChatGPT is a valuable tool, but it surely doesn't replace human judgment. Its learning models are based on consuming existing content — some of which contain mistakes and errors.

No matter what code snippet is generated by ChatGPT, you still need to apply your judgment to ensure it's working for your problem. ChatGPT generates snippets based on code written in the past, so there's no guarantee that the generated code is suitable for your particular situation. As with any snippet you find on StackOverflow, you still have to ensure your intention was fully understood and that the code snippet is suitable for your program.

Ultimately, we can't blindly copy/paste code snippets from ChatGPT, and the consequences of doing so could be severe.

2. ChatGPT can't problem-solve

Problem-solving is an essential skill that developers need to have, which is why machine-learning and text-based tools won't take over developer jobs anytime soon.

As a developer, your job involves understanding a problem, coming up with several potential solutions, then using a programming language to translate the optimal solution for a computer or compiler. While machine learning tools can help us type code faster, they can't do problem-solving for us.

While ChatGPT can enable many people to become better, more efficient developers, it's not capable of building large-scale applications for humans. In the end, we still need human judgment to discern between good and bad code. Even if we’re receiving help when writing code, we're not running out of big problems to solve.

Relying on ChatGPT to solve your problems with plagiarized code is dangerous. For one thing, inserting code you copied into your applications indiscriminately introduces great security, legal, and ethical risks, even if you're borrowing from a machine-learning tool. Besides, the tech industry still wants critical thinking developers, and you're not going to convince anyone you have those attributes by stealing code.

Suffice it to say, plagiarism is definitely not the correct use of ChatGPT.

3. ChatGPT doesn't have multiple perspectives

ChatGPT has a limited perspective. Its suggestions are based on the data it is trained with, which comes with many risks.

For one, if ChatGPT mistakes a highly repeated code snippet as a best practice, it can suggest and perpetuate a vulnerability or inefficiency.

ChatGPT is fully capable of generating incorrect answers, but like any other answer, it will do so with utmost confidence. Unfortunately, no metric helps manage expectations about the potential error in a response. This is a disadvantage against other sources we visit for guidance. Sites like StackOverflow or GitHub are capable of giving us more multidimensional data. We can validate others' suggestions by looking at their context, responses, upvotes, etc. In this sense, other sources are better equipped to touch on the nuances of real-world problems.

ChatGPT's limited perspective can make it something of an echo chamber, which can be very problematic.
We've long known that machine learning algorithms can inherit bias, so AI is vulnerable to adopting harmful biases like racism, sexism, and xenophobia. Despite the guardrails that OpenAI has implemented, ChatGPT is also capable of inheriting bias. (If you're interested, reporter Davey Alba discussed ChatGPT's susceptibility to bias on Bloomberg.)

All in all, we have to take every ChatGPT response with a massive grain of salt — and sometimes, it might be easier just to write your code from scratch than to work backward to validate a generated code snippet.

4. ChatGPT can't get you hired

Though it can generate a code snippet, ChatGPT is not the end of coding interviews. Besides, the majority of the coding interview consists of problem-solving — not writing code. Writing code will only take about 5-10 minutes of a 45-minute coding interview. If you’re curious about how to prepare for interviews efficiently, check out this full breakdown of the Amazon coding interview.

The rest of the coding interview requires you to give other hireable signals. You still need to ensure that you’re asking the right questions to articulate and understand your problem and narrating your thought process to demonstrate how you narrow your solution space. ChatGPT can't help you with any of this. However, these critical thinking and problem-solving skills carry just as much weight in your hireability as your coding competency.

Don't rely on ChatGPT too heavily. Instead, rely on your knowledge and skills to get the job!

Start leveraging AI for yourself

Machine learning tools help us perform tasks more efficiently, but they don't replace our need to think. Sometimes they're right, and other times they're incredibly (and hilariously) wrong. ChatGPT will help you immensely in your daily life, but it stops well short of doing your job for you.

While assistants like Siri and Alexa can help us with basic tasks, they can't help us with complex efforts like making substantial life changes. Similarly, ChatGPT can't help us with complex problems, nor can it replace innovation. But these technologies help alleviate menial tasks that distract us from tackling more ambitious issues (such as improving AI technologies).

As a developer, you shouldn’t stop investing in your learning or long-term coding career. If anything, be open to incorporating these tools into your life in the future. If you are interested, you can learn how to leverage AI for yourself.

If you're curious about AI technologies, there has never been a better time to learn ML. At Educative, our hundreds of courses, projects, and Skill Paths include these three terrific offerings to learn ML:

With these resources, you'll get up to speed quickly through hands-on work with ML techniques and tools, including deep learning, NLP, GPT-3, and integrating the OpenAI API.

Happy learning!

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