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Leveraging Generative AI for Better Apps

Pablo Amadei

Head of Application Development , Synechron

Artificial Intelligence

Artificial Intelligence (AI) is changing the way we build user interfaces.

It’s improving the user experience by creating more personalized solutions, and making codebase faster, safer and more accessible. AI can find and fix errors, ensure accessibility standards, and streamline performance. This leads to faster applications with fewer errors – enabling developers to spend more time building features that solve real problems. And, with AI handling the initial coding and scanning for errors, humans can spend vital time on the oversight essential for the development of a fully functional product.

Now, as the next step in AI, generative AI (GenAI) is about to deliver even better user interfaces.

Technologies

There are a number of terms we should all know if we’re planning to use GenAI tools:

  • Generative AI (GenAI) is a branch of AI that’s focused on using machine learning (ML) models, like OpenAI's GPT-3, to generate new content such as text, images, videos, audio and code. Generative models aim to predict the probability of the next word based on previous words it has seen. GenAI can automate repetitive tasks like generating simple UI components (login, forms, pricing pages, among other things) or boilerplate code (setting up your state management store or generating a mock Json Api response).
  • Generative Pre-trained Transformer (GPT) is a type of LLM that uses deep learning. It was released in 2020, by OpenAI, to produce human-like text. The latest OpenAI releases are GPT-3 and GPT-4, which can be accessed by APIs, or a user interface called ChatGPT. This year, OpenAI also launched a ChatGPT Enterprise subscription – for businesses who need more control over their data and enterprises seeking to manage their end users.
     

Tools

In terms of tools, there are a few that can assist developers with GenAI right now (it's important to mention that more tools may become available as the industry develops):

  • ChatGPT: Released by OpenAI and powered by the latest version of GPT-3 and GPT-4, Chat GPT is without doubt the most famous chatbot, and reached more than 100 million active users a couple of months after its launch. OpenAI offers a playground user interface, launched before ChatGPT, and allows control of some settings, such as temperature, max length or a specific model. ChatGPT is not the only GenAI chatbot on the market though; models like ‘Claude’ by Anthropic and ‘Bard’ by Google, are already catching up. Although all these models provide similar results, there is a lot of debate around the risks, like security, privacy and safety.
  • GitHub Copilot: GitHub Copilot is an AI pair programming assistant, built to help developers code more efficiently. Announced by GitHub in June 2021, and powered by OpenAI Codex, it’s a descendant of GPT-3. Copilot works with all major programming languages and its ‘autocomplete’ feature – which suggests code on the fly as developers write – is the main selling point. Copilot can assist by building regular expressions, working with unfamiliar programming languages and generating boilerplate code. Currently, GitHub is working on Copilot X, which will adopt GPT-4 to improve Copilot. They aim to add chat, voice, automated pull request review, CLI suggestions, and an improved chatbot to answer project questions.
  • CodeWhisperer: Recently announced by Amazon, CodeWhisperer stands out as a free alternative to Copilot. This tool can be installed directly on Visual Studio Code or Jet Brains and auto-suggests code as the developer writes. It also has some interesting features, such as running security scans that can help detect hard-to-find vulnerabilities – while offering code suggestions to fix them immediately.
  • Codeium: Finally, Codeium bills itself as a “modern coding superpower” that helps developers with automated suggestions. It offers a chat assistant that can write code and answer questions, such as “How do I undo a commit”, as well as refactoring code to fix bugs. As a selling point, the enterprise tier offers a self-hosted container that prevents data from leaving the on-prem server, as well as additional local training to fine-tune the model, personalizing it with existing code and useful statistics for business teams.
     

Leveraging Generative AI for business

Now that we know the technologies and tools teams can utilize, we can start talking about what is practically possible with Generative AI.

  • Personalization and recommendation

GenAI can create a more engaging and relevant user experience by suggesting pertinent content or product recommendations, based on user and company data. Tools like GPT can analyze text inputs and user data, remember conversation and generate predictions, based on previous actions or preference. For instance, companies can use an AI travel assistant that will remember past trips, places, restaurants – and even transport preferences like preferring taxis to Ubers. Now, with these technologies, teams can build more engaging applications with direct access to the power of AI.

  • Prototyping

Rapid prototyping enables faster development cycles – and one area where new AI models will accelerate development is during prototyping. These AI models can help with the heavy lifting of starting a new project or using a new programming language or library. With advanced AI models, such OpenAI's GPT-3 and Codeium, teams can quickly build prototypes by describing the models that they would like to create, which programmers can then review and edit – for example, building forms, a responsive CSS grid layout, or translating Angular code into React. The newest development of GTP, in terms of Vision recognition (aka GPT-4Vision), will even allow the generation of lines of code by uploading a sketch of a UI.

  • Improving code

Another area where Generative AI is improving applications is in helping teams ship cleaner and safer code. Tools like Copilot can already suggest code or generate unit tests for your code. However, more powerful tools like DiffBlue can analyze the code base (currently only supporting Java) and write tests autonomously. Other tools like Copilot and Amazon CodeWhisperer can also assist in generating tests or code improvements.

These AI models will also assist with complex areas such as accessibility and security, by scanning code, suggesting changes, and writing a better version of a piece of code. These tools can leverage all the good accessibility best practices covered by WCAG principles or security vulnerabilities outlined by the Open Web Application Security Project (OWASP).

  • Developer Experience

There are a couple of areas where AI can assist developers, and one of them is in improving the overall Developer Experience (DX), which can ultimately impact how quickly a team can deliver code. One such area is documentation. AI can automatically generate documentation, summarizing the UI components, JS functions, CSS classes, data schemas and APIs used, in a fraction of the time it would take for a team of people to do it manually. This allows teams to spend less time writing documentation and more time building features.

  • Learning

Another indirect benefit of GenAI on how we build front-end applications is around learning. All these new technologies and tools help developers improve their coding skills. Now you can learn faster by creating your own learning assistant, using tools like ChatGPT or Bard and clever prompting, for instance: “You are a learning assistant. Based on the pareto principle, explain Kubernetes.” Learning platforms can build customized learning paths, project-based learning, interactive examples and automated quizzing, based on the topic the developer is learning. AI-power learning is going to be more engaging, effective and help developers stay up to date with all the latest trends.

GenAI is empowering developers

Generative AI is changing the way we develop front end apps, empowering developers with enhanced productivity, creativity and problem-solving abilities. It will continue to give dev teams the power to build whatever they envision. Previously only accessible to large tech companies, these AI tools are now available to any team, enabling startups and enterprises alike. GenAI can uplift developers by automating repetitive tasks so they can focus on more complex challenges.

At Synechron, we're already helping teams to leverage the power of generative AI. This includes GenAI model validation that compares multiple AI models, and a GenAI tool that uses a database to source solutions and responses for report generation – both of which save significant time and resources.

Contact us to find out more about how we can help your business.

The Author

Rachel Anderson, Digital Lead at Synechron UK
Pablo Amadei

Head of Application Development

Pablo Amadei is the Head of Application Development at Synechron. He has solid experience in software development, finance and entrepreneurship. He is passionate about web development, cutting-edge technologies and code craftsmanship. He has led teams across several complex projects by leveraging latest technologies and fostering a culture of innovation and continuous learning. He is excited by the recent developments in AI, and in particular how Generative AI tools will shape the future landscape of Software Development.

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