As they say, “AI won’t take your job, but someone using AI will”.
Introduction - generative AI and code
The future of application development is on a transformative journey, with the convergence of artificial intelligence (AI) and nocode platforms redefining what's possible. While AI promises to expedite app creation, its full-scale adoption for code generation is not without its share of challenges. This article explores a balanced approach where AI-generated code complements rather than replaces human understanding and input.
The Current State of Application Development
Application development is no longer exclusive to those with extensive coding knowledge. The introduction of AI-powered tools has transformed app creation, with algorithms that can predict code, automate bug fixes, and optimize performance. Parallel to this, nocode platforms have surfaced, offering a launchpad for innovative ideas without the steep learning curve of traditional programming. Recently, AI is also powering nocode platforms that make it even easier and faster to generate nocode applications, start with a prompt passed to AI assistants.
“A recently published survey by GBH Insights LLC, which does business as GBK Collective, found that 78% of companies expect to use AI for software development within the next three to five years.” 1
The Role of AI in Application Development
Today, everything moves at hyper speed, encouraged through processes like Design Thinking and agile methodologies. The more (and faster) you iterate, the better the result.
So, “Application Development” is not just the code, it starts from the requirements, flowing to the User Experience (UX), User Interface (UI), the code and testing, getting working products into the hands users, gathering feedback and of course, iterating.
We already see AI coming into each phase of these processes with intent to make it easier, faster better: whether AI can help generate specification, design UX/UI, create code, generate tests, automate tests etc
Application development is being redefined by AI through three innovative patterns:
1. AI Co-Pilots: Tools like GitHub Copilot assist developers by suggesting code snippets, improving efficiency and allowing developers to focus on complex tasks. As virtual collaborators, they offer quick solutions, reducing coding time for routine tasks. These co-pilots have also made their way into UX/UI to tools like Figma & Canva, all aimed it making it easier to create and design better solutions.
2. AI in Full Code Generation: AI accelerates development by generating substantial code segments, sometimes for entire applications, from defined requirements. This can greatly speed up production but may result in a complex codebase that's challenging to maintain.
3. AI-Driven Nocode Platforms: AI is simplifying app creation for non-developers by translating business and user requirements into both full functioning apps and functional app components on nocode platforms. This expands app development to a broader audience, making it more accessible and user-friendly. The key difference with Nocode apps generated by AI and a Full Code solution, is that the majority of the code on the nocode solution is hidden, invisible and does not have to be touched by the user, it is provides, maintained and enhanced by the nocode platform.
In essence, AI is streamlining the development process from manual UX and code creation to application configuration, embodying a transformative force that enhances efficiency and broadens participation in app development. However, leveraging AI effectively requires a balance to ensure the resultant code is manageable and the development process remains transparent.
The Pitfalls of Over-Reliance on AI for Code Generation
Heavy reliance on AI in coding processes can cause a disconnect between the developers and the code itself. Debugging, understanding, and maintaining a large, AI-generated codebase can become an unwieldy task, which may slow down the very efficiency it aims to improve.
We asked chatGPT how many lines of code it expected to generate if it built a custom CRM system encompassing customer deal tracking, scheduling, activity tracking, and task tracking.. The answer was “custom-built CRM system using AI like ChatGPT for code generation could be in the range of 5,500 to 15,000 lines of code” (it actually broke it down into each of the functions in the CRM,
Just imagine handing these thousands of lines of code to a development team to understand, enhance and maintain, when no-one in the team would have seen this code before?
Note this is without any customization to UX, UI or core functionality, how will you make those changes?
A Balanced Approach: The Symbiosis of AI and Nocode Development
The ideal app development strategy employs AI not to eliminate the role of developers but to augment it. AI can quicken the pace of development, while nocode interfaces allow developers to integrate these AI elements into a cohesive application. This strategy acknowledges the importance of maintaining a human touch within the automated landscape.
The Hybrid Model: Integrating AI-Generated Code with Nocode Platforms
In the hybrid model, AI-generated and nocode components come together to streamline app development:
1. AI-Powered Nocode Application Creation:
Platforms leveraging AI can take a simple prompt and rapidly construct the framework of a nocode application, encompassing data models, user interfaces, and business logic. This method allows developers to harness AI's speed for application assembly without delving into the code, simplifying enhancement and maintenance.
2. Integrating AI-Generated Custom Code:
When nocode platforms meet limitations, AI-generated custom code can be introduced for precise needs:
a) Front-End Enhancements: AI can code specific front-end features, adding sophisticated user experiences beyond nocode offerings.
b) Back-End Extensions: On the back end, AI can help (co-pilot) generate functions for complex data operations, enriching the application's backend services where nocode falls short.
This model offers the ease of nocode development with the customizability of AI-generated/co-pilot code, ensuring flexible and robust application development. A key factor here is that the thousands of lines of code provided by the no-code platform are robust and performant, and they will be maintained by the platform itself.
Best Practices for AI-Generated Code in Nocode Platforms
So for the AI code you generate, be sure that you can answer these questions:
Does it do what we want?
Can our team enhance the code?
Can our team maintain the code?
Implementing AI-generated code within nocode platforms calls for a disciplined approach:
Keep it Simple Stupid (KISS): Keeping AI-generated code pieces small and manageable ensures they are easily integrated and maintained.
Train/Prompt the AI to understand the No Code platform: Tell your code generation about the no code platform - so the generated code can leverage the nocode platform’s APIs to allow a seamless integration between the custom code and the nocode platform
Quality Assurance: Rigorous testing of AI-generated code is essential to maintain the integrity of the application.
The Future is Hybrid: Predictions and Trends
Looking ahead, we believe a hybridized approach to app development is poised to become the norm. While AI has the ability to generate billions of lines of full code solutions, it’s just not maintainable by a limited amount of resources, developers. The development community will likely witness a greater synergy between AI's analytical prowess and the human developer's creative and strategic capabilities. This partnership promises a future where app development is not just faster but also more accessible and adaptable to change.
Conclusion
As we embrace this new horizon, the fusion of AI with nocode development is set to make app creation more inclusive, efficient and robust. This hybrid approach democratizes the development process, allowing for rapid iteration and deployment while ensuring the final products are robust and user-friendly. By upholding the hybrid model, the development community can look forward to a future of innovation, where the barriers to entry are low, and the potential for growth is unlimited.
About the Author
Adam Ginsburg is Founder & CEO of Buzzy. Buzzy is an AI powered no code platform that allows you to turn an idea in to an app, instantly. Try it here. In addition to working at Buzzy, Adam is a husband, father and surfer. Adam was a co-founder of Aptrix, which IBM acquired and became IBM Web Content Manager.
About Buzzy
Buzzy is an AI powered nocode platform that allows your to start with a prompt and generate a full stack application in minutes. You can extend the solutions using custom client or server-side code, that can be written by hand or an AI co-pilot. In addition to generating the application Buzzy allows you to generate a Figma file, that you can customize the high-fidelity design and re-publish the changes instantly, without having to write any code. Rego for Free Buzzy webinars here