The ripples of GenAI are likely to be felt for the foreseeable future and beyond, but it’s not all doom and gloom for technologies that intersect with it. The GenAI paradigm shift is especially exciting for complementary technologies like low-code development. Together, low-code and GenAI development move enterprises away from resource-intensive traditional coding requirements. Think:
- shorter timelines
- less risk
- increased innovation.
Low-code uses modeling, templates, and visual design as productive development abstractions. GenAI produces code and app components based on written or verbal prompts from developers. Together, a GenAI-built low-code model is easier to understand and improve than thousands of lines of AI-generated traditional code.
Creating GenAI opportunities within a low-code context reduces time and cost commitments. Developers with more time can focus on higher-level concerns. That results in increased quality and consistency.
Faster, smarter development
With low-code, enterprises can visually represent software development and build AI-augmented applications with drag-and-drop simplicity.
GenAI achieves similar results but in a completely different way. Low-code provides model-based development and technology abstraction (visual building blocks). GenAI leverages large language models trained on vast data sets to generate unique, contextual outputs based on prompts.
Technologists argue whether GenAI is a paradigm shift or a tool developers use to enhance and simplify coding. The truth likely falls somewhere in between.
In any case, software development is rapidly changing because of GenAI. Expectations have shifted to:
- more personalized, robust experiences
- accessible to more businesses
- shorter development windows.
GenAI: The ultimate scale booster?
One thing that makes GenAI different is scale. As an example, Meta Llama 3 queries generate conditional output by interacting with a model trained on over 15 trillion language tokens. That’s enough words and characters to get people wondering if we have enough data to train new models.
“Interacting” here is a way of saying that GenAI leverages advanced deep learning algorithms to:
- find patterns
- generalize new behavior
- create contextual and human outputs.
These conditional outputs are delivered as written or visual content, code, or data.
Additionally, GenAI produces content autonomously—unless you count the prompt. With good data, you can enter a prompt and receive a contextually accurate, relevant response. That response can take the form of text, images, and even complete domain models.
Opinions on use cases and ethics differ, but the end impact on business process timelines will be undeniable.
GenAI across the SDLC
Generative AI-enabled development platforms can benefit the entire software development life cycle (SDLC). That includes both traditional coding and low-code. For instance, GenAI can validate code for traditional implementation and automate software application deployment through a low-code interface.
However, traditional coding often equates to longer timelines, higher risk, and slower innovation cycles. Those cut into GenAI’s value for enterprises reliant on them. GenAI can boost productivity, but it can’t solve all the issues inherent to traditional development. These problems range from technical debt to skillset specialization silos.
Here are some examples of GenAI’s impact within the SDLC:
- Research and requirements gathering: Accelerate discovery with predictive analytics and prompt-based tools.
- Design: Lay the foundation for schema, taxonomy, and architecture, assisting with UX prototyping.
- Development: AI-assisted application development (AIAD) tools such as Mendix AI Assistant (Maia) can suggest, validate, and improve code.
- Testing: Generate test cases and automate quality assurance with GenAI.
- Deployment: Optimize system and timing strategies to automate deployment.
- Post-deployment: Perform bug triage, schedule maintenance, improve the customer experience, and provide user support.
Connect GenAI with low-code
Businesses realize they must integrate GenAI with their systems before benefiting. Disconnected, siloed output—no matter how impressive in its generation—is still disconnected. It’s not common or easy to deliver access to technology that checks all the following boxes:
- Integrates with existing systems and cloud setups
- Deploys copilots and allows full lifecycle AI integration
- Optimized for future innovation
Thankfully, low-code is different.
Adapting AI into a low-code framework enables different features and approaches, from early bots to machine learning and smart apps. Low-code platforms curate and connect the technologies that create software development efficiencies, including GenAI.
Low-code connects vast, time-saving apps, allowing developers to create custom solutions quickly.
Combining GenAI with low-code boosts productivity and gives enterprises more flexibility. Benefits include:
- Giving experienced developers more time to improve business outcomes.
- Helping emerging developers produce better outcomes with GenAI assistants.
- Providing business technologists a way to contextually collaborate.
A huge advantage to low-code development is that it integrates technologies while expediting and enhancing development quality. A low-code platform should also make it easier for developers to incorporate GenAI and other technology innovations into their applications. Traditional development can’t say the same.
When developers utilize GenAI in a low-code IDE, they have tools to create better models and application components faster. AI-assisted development (AIAD) tools add checks and balances, suggest shortcuts, and can even suggest solutions.
Low-code platforms use a visual framework because it eases the software development burden. Similarly, AIAD features can guide and assist developers and generate code. GenAI helps expedite and improve the building stage of the development process. But that’s just the start. Opportunities exist during requirements gathering, design, code validation, quality assurance, and more.
Stay secure with GenAI and low-code
With GenAI comes a new wave of security, compliance, and data protection questions.
Low-code platforms are responsible for connecting and managing individual GenAI applications. So, they should also ensure continuity, maintain compliance standards, and protect data.
Low-code can also enable GenAI by empowering developers to:
- plan, manage, deploy, and integrate technology
- reduce thrash, security concerns, and downtime
This helps with scale and governance.
Prepare for the future and the present
GenAI isn’t new, but we’re still in early days. The possibilities GenAI can present on its own are exciting. But, paired with low-code, GenAI solutions are attainable and ready to launch quickly.