The Role of AI in Low- and No-Code Development

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Low-Code Development: Elevating the Engineering Experience With Low and No Code.

The advent of large language models (LLMs) has led to a rush to shoehorn artificial intelligence (AI) into every product that makes sense, as well as into quite a few that don't. But there is one area where AI has already proven to be a powerful and useful addition: low- and no-code software development.

Let's look at how and why AI makes building applications faster and easier, especially with low- and no-code tools.

AI's Role in Development

First, let's discuss two of the most common roles AI has in simplifying and speeding up the development process: 

1. Generating code 
2. Acting as an intelligent assistant

AI code generators and assistants use LLMs trained on massive codebases that teach them the syntax, patterns, and semantics of programming languages. These models predict the code needed to fulfill a prompt — the same way chatbots use their training to predict the next word in a sentence.

Automated Code Generation

AI code generators create code based on input. These prompts take the form of natural language input or code in an integrated development environment (IDE) or on the command line. Code generators speed up development by freeing programmers from writing repetitive code. They can reduce common errors and typographical mistakes, too. But similar to the LLMs used to generate text, code generators require scrutiny and can make their own errors. Developers need to be careful when accepting code generated by AI, and they must test not just whether it builds but also that it does what the user asks.

gpt-engineer is an open-source AI code generator that accepts natural language prompts to build entire codebases. It works with ChatGPT or custom LLMs like Llama.

Intelligent Assistants for Development

Intelligent assistants provide developers with real-time help as they work. They work as a form of AI code generator, but instead of using natural language prompts, they can autocomplete, provide in-line documentation, and accept specialized commands. These assistants can work inside programming tools like Eclipse and Microsoft's VS Code, the command line, or all three.

These tools offer many of the same benefits as code generators, including shorter development times, fewer errors, and reduced typos. They also serve as learning tools since they provide developers programming information as they work. But like any AI tool, AI assistants are not foolproof — they require close and careful monitoring.

GitHub's Copilot is a popular AI programming assistant. It uses models built on public GitHub repositories, so it supports a very wide variety of languages and plugs into all the most popular programming tools. Microsoft's Power Platform and Amazon Q Developer are two popular commercial options, while Refact.ai is an open-source alternative.

AI and Low and No Code: Perfect Together

Low and no code developed in response to a need for tools that allow newcomers and non-technologists to quickly customize software for their needs. AI takes this one step further by making it even easier to translate ideas into software.

Democratizing Development

AI code generators and assistants democratize software development by making coding more accessible, enhancing productivity, and facilitating continuous learning. These tools lower the entry barriers for newcomers to programming. A novice programmer can use them to quickly build working applications by learning on the job. For example, Microsoft Power Apps include Copilot, which generates application code for you and then works with you to refine it.

How AI Enhances Low- and No-Code Platforms

There are several important ways that AI enhances low- and no-code platforms. We've already covered AI's ability to generate code snippets from natural language prompts or the context in a code editor. You can use LLMs like ChatGPT and Gemini to generate code for many low-code platforms, while many no-code platforms like AppSmith and Google AppSheet use AI to generate integrations based on text that describes what you want the integration to do.

You can also use AI to automate preparing, cleaning, and analyzing data, too. This makes it easier to integrate and work with large datasets that need tuning before they're suitable for use with your models. Tools like Amazon SageMaker use AI to ingest, sort, organize, and streamline data. Some platforms use AI to help create user interfaces and populate forms. For example, Microsoft's Power Platform uses AI to enable users to build user interfaces and automate processes through conversational interactions with its copilot.

All these features help make low- and no-code development faster, including in terms of scalability, since more team members can take part in the development process.

How Low and No Code Enable AI Development

While AI is invaluable for generating code, it's also useful in your low- and no-code applications. Many low- and no-code platforms allow you to build and deploy AI-enabled applications. They abstract away the complexity of adding capabilities like natural language processing, computer vision, and AI APIs from your app.

Users expect applications to offer features like voice prompts, chatbots, and image recognition. Developing these capabilities "from scratch" takes time, even for experienced developers, so many platforms offer modules that make it easy to add them with little or no code. For example, Microsoft has low-code tools for building Power Virtual Agents (now part of its Copilot Studio) on Azure. These agents can plug into a wide variety of skills backed by Azure services and drive them using a chat interface.

Low- and no-code platforms like Amazon SageMaker and Google's Teachable Machine manage tasks like preparing data, training custom machine learning (ML) models, and deploying AI applications. And Zapier harnesses voice to text from Amazon's Alexa and directs the output to many different applications.

Figure 1. Building low-code AI-enabled apps with building blocks

Examples of AI-Powered Low- and No-Code Tools

This table contains a list of widely used low- and no-code platforms that support AI code generation, AI-enabled application extensions, or both:

Table 1. AI-powered low- and no-code tools

Pitfalls of Using AI for Development

AI's ability to improve low- and no-code development is undeniable, but so are its risks. Any use of AI requires proper training and comprehensive governance. LLM's tendency to "hallucinate" answers to prompts applies to code generation, too. So while AI tools lower the barrier to entry for novice developers, you still need experienced programmers to review, verify, and test code before you deploy it to production.

• Developers use AI by submitting prompts and receiving responses. Depending on the project, those prompts may contain sensitive information. If the model belongs to a third-party vendor or isn't correctly secured, your developers expose that information.
• When it works, AI suggests code that is likely to fulfill the prompt it's evaluating. The code is correct, but it's not necessarily the best solution. So a heavy reliance on AI to generate code can lead to code that is difficult to change and represents a large amount of technical debt.

AI is already making important contributions toward democratizing programming and speeding up low- and no-code development. As LLMs gradually improve, AI tools for creating software will only get better. Even as these tools improve, IT leaders still need to proceed cautiously. AI offers great power, but that power comes with great responsibility. Any and all use of AI requires comprehensive governance and complete safeguards that protect organizations from errors, vulnerabilities, and data loss.

Conclusion

Integrating AI into low- and no-code development platforms has already revolutionized software development. It has democratized access to advanced coding and empowered non-experts so that they can build sophisticated applications.

AI-driven tools and intelligent assistants have reduced development times, improved development scalability, and helped minimize common errors. But these powerful capabilities come with risks and responsibilities. Developers and IT leaders need to establish robust governance, testing regimes, and validation systems if they want to safely harness AI's full potential.

AI technologies and models continue to improve, and it's probable that they will become the cornerstone of innovative, efficient, and secure software development. See how AI can help your organization widen your development efforts via low- and no-code tools.

This is an excerpt from DZone's 2024 Trend Report, Low-Code Development: Elevating the Engineering Experience With Low and No Code.

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