I tried using ChatGPT to help with a common coding issue when working on CRM applications and merging customer data sources. I asked ChatGPT, “Given two lists of names, write Python code to find near matches of the names and compute a similarity ranking.” ChatGPT replied, “You can use the FuzzyWuzzy library in Python to find near matches and compute similarity rankings between names.” ChatGPT then displayed code to interface with FuzzyWuzzy and included examples to help demonstrate results.
Now, there are debates about how smart ChatGPT is, whether it can write secure code, and why it should attribute its sources. But ChatGPT’s effectiveness is causing many people to consider how generative AI will change people’s creative work in marketing, journalism, the arts, and, yes, software development.
ChatGPT already reached more than 100 million users, and Microsoft is embedding it in Bing and other Office applications. Other generative AI competitors in search platforms include Google’s Bard, and developers can test code-generating AIs such as AlphaCode and GitHub Copilot. A wave of SaaS products, tech platforms, and service providers are integrating ChatGPT capabilities.
If you’re a software developer or a devops engineer, you might experiment with generative AI tools and wonder what it will mean for your profession and how it will change your work.
Remember when you installed your first Amazon Alexa or Google Assistant in your home, expecting it to be as smart and responsive as Star Trek’s computer? It helps you do simple tasks such as set alarms, add items to shopping lists, share the weather forecast, or update you on today’s news, but it’s unlikely to answer more complex questions accurately.
For now, generative AI can help fill gaps and accelerate implementing solutions within the software development life cycle, but we will still need developers to drive appropriate experiences.
Software development has many generational improvements in languages and platforms. Many tools increase a developer’s productivity, improve code quality, or automate aspects of the delivery pipeline. For example, low-code and no-code platforms can help organizations build and modernize more applications, but we’re still coding microservices, developing customer-facing applications, and building machine learning capabilities.
Developers must also consider how ChatGPT raises the bar on user expectations. The keyword search box in your app that isn’t personalized and responds with disappointing results will need an upgrade. As more people are amazed by ChatGPT’s capabilities, employees and customers will expect AI search experiences with natural language queries and apps that answer questions.
Generative AI can also improve workflow and support hyperautomation, connecting people, automation, and AI capabilities. I think about smart health applications, where doctors can ask AI questions about a patient’s condition, the AI responds with similar patients, and the app provides options for doctors that automate ordering procedures or prescriptions.
So, where can software developers leverage generative AI today? It’s easy to see its usefulness in finding coding examples or improving code quality. But product managers and their agile development teams should validate and test their use cases before plugging a generative AI into their application.
ChatGPT is more than a shiny object, but like any new technology, software developers and architects will need to validate where, when, and how to use generative AI capabilities.
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