GenAI has become a cornerstone in revolutionizing how we create everything from emails and videos to brand new applications. Its foundation is grounded in Generative Adversarial Networks (GANs) technology, which trains two neural networks to compete against each other to generate original content from an existing dataset.
Using artificial intelligence algorithms and natural language processing and understanding, it creates new content from a model trained on extensive datasets to produce content that mirrors the original data in style or structure. This includes images, text, and simple applications.
Uncover the key differences between generative AI vs. AI
GenAI has the potential to benefit development by streamlining code generation, enhancing creativity, and accelerating time-to-value. For developers, generative AI speeds up the processes used to code unique, innovative solutions. It simplifies user interface design, code generation, and validation while enabling rapid app prototyping, and the development of groundbreaking features.
By freeing developers from mundane tasks, GenAI unleashes their creative potential, enabling them to focus on tackling complex problems and driving innovation within their projects.
At a glance, generative AI typically works like this:
Here’s a closer look at each stage.
Generative AI works by training machine learning models on large datasets so they learn patterns, structure, and relationships in that data. During training, the model processes millions of examples and adjusts its internal parameters to improve how well it can predict what comes next in a sequence. Once trained, it can generate new outputs that resemble what it learned from, including text, images, and code.
Most modern generative AI development is powered by transformer-based models, including large language models (LLMs). When someone enters a prompt, the model interprets the context and produces an output by predicting the most likely next token step by step. This is how GenAI can draft a paragraph, generate a code snippet, or create a description that matches the intent and style implied by the input.
In enterprise generative AI, teams build on those base models with additional controls and tuning. Organizations may fine-tune a model on proprietary data to better match their domain, brand, and workflows. Many also use retrieval-augmented generation (RAG) to ground outputs in trusted internal sources, which helps improve relevance, reduce hallucinations, and support governance requirements.
In practical applications of generative AI, this flow is usually embedded directly into products and development tools. A user prompt triggers the model, the model generates a draft output, and the application applies guardrails, formatting, and validation before returning results. In software delivery, this can support tasks like code completion, UI generation, documentation drafting, and personalized user experiences, helping teams move faster while keeping quality and control in focus.
GenAI shines in content generation. With the ability to understand context and language nuances, generative artificial intelligence algorithms can automatically produce high-quality written content such as product descriptions or blog articles. They can also create images, GIFs, videos, and code, combining them all with written content to deliver websites, applications, augmented reality, and more. This not only saves valuable time for content creators but also ensures consistency and accuracy across multiple platforms, opening up a new world for developers.
One practical application of generative AI in app development is the creation of personalized user interfaces. Using prompts and GenAI algorithms, developers can analyze user behavior, preferences, and interaction patterns to dynamically produce UI elements that are tailored to individual users. The AI can suggest layout adjustments, color schemes, and even feature placements that align with the user's habits and preferences, potentially providing a more intuitive, efficient, and enjoyable interaction with the application. This not only saves time for developers but also ensures a seamless engagement for end-users.
Another practical use for generative AI in app development is code completion. It can predict and fill in the next part of the code based on the context of what the developer is writing. This not only speeds up the coding process but also helps reduce errors, suggests best coding practices, and potentially introduces more efficient ways to achieve a task. AI-powered code completion learns from vast amounts of code to offer relevant, context-aware suggestions, improving developer productivity and code quality.
OutSystems’ developer relationship team used GenAI to revolutionize the user experience for their Japanese community, the company's second-largest developer group. This initiative was born out of the necessity to include Japanese-speaking developers in global community conversations, overcoming the language barrier that previously led to a sense of exclusion. OutSystems embarked on this journey, driven by the principle that "giving up" was not an option, aiming to integrate Japanese developers seamlessly into the community.
The project involved a meticulous selection process for the most suitable LLMs and intricate prompt engineering to ensure high-quality, real-time translations without the addition of extraneous information. OutSystems evaluated several LLMs against specific criteria, including translation quality, performance, and cost. ChatGPT4 emerged as the preferred choice for its ability to effectively handle complex, technical text and HTML content.
Integration with the OutSystems Developer Cloud (ODC) and API connections to various LLMs facilitated the real-time translation of community forum posts. Additionally, a strategic caching mechanism was implemented to optimize costs and efficiency, allowing for the reuse of translations and reducing the need for repeated API calls.
The project's success has improved the engagement and experience of Japanese-speaking developers and set the stage for expanding AI-powered translation services to include more languages, furthering OutSystems’ mission of creating a barrier-free, inclusive community. This highlights the transformative potential of GenAI in overcoming language barriers and fostering global collaboration and engagement within developer communities.
Generative AI is revolutionizing application development across various industries by automating code generation, enhancing design processes, and providing intelligent solutions that significantly improve efficiency, creativity, and customization in apps.
In one report, McKinsey concluded that GenAI has the potential to add $2.6 trillion to $4.4 trillion annually in value across 63 use cases that include interactions with customers, producing creative content for marketing and sales, and drafting computer code based on natural-language prompts.
Here are examples of how generative AI is being used in financial services, manufacturing, healthcare, and media and entertainment.
There are many use cases for GenAI in financial services applications. One example is software that automates the creation of financial reports and analyses. By inputting raw financial data, the AI tool can generate comprehensive reports, including performance analysis, trend forecasts, and risk assessments. For example, a generative AI system could analyze a company's financial data over time and produce an in-depth report highlighting key financial health indicators, predictive revenue models, and investment risks, significantly reducing the time and effort required for manual financial analysis.
Another use of GenAI in this industry is an application that provides personalized financial advice to users. For example, a financial services app could use generative AI to offer users tailored advice on how to allocate their investments, or suggest specific financial products that align with their individual profiles. This can significantly enhance the user experience and aid in more informed financial decision-making.
See how the OutSystems AI development platform transforms financial services
In manufacturing, there's often a need for custom software solutions that cater to specific production processes, machinery, or product lines. GenAI can assist developers by automatically generating code or modules based on the specific requirements of a manufacturing process. For example, if a manufacturer needs a unique monitoring system for a production line, generative AI can help create the foundational code, which developers can then refine and customize.
GenAI can also be used to develop sophisticated algorithms that implement improvements in manufacturing processes. When these algorithms are integrated into software, manufacturers can continuously enhance efficiency, reduce waste, and adapt to changing market demands or resource availability.
Explore manufacturing solutions
Generative artificial intelligence is making a significant impact in healthcare. It’s being used in software that analyzes vast amounts of medical images to detect patterns and anomalies that human observers might miss. This has led to more accurate diagnoses and improved patient outcomes. It’s also been integrated into software projects to assist in drug discovery. Researchers can identify potential drug candidates faster by analyzing large datasets and predicting molecular structures. This breakthrough technology has the potential to accelerate the development of life-saving medications.
Another area GenAI is being embedded in healthcare is personalized medicine. By analyzing an individual's genetic data and medical history, software integrated with AI can help build tailored treatment plans that take into account specific genetic variations and risk factors. This enhances patient care and contributes to more efficient resource allocation within healthcare systems.
Create healthcare apps with OutSystems’ AI development platform
GenAI-powered platforms are transforming how content is created across different domains, offering tools that augment human creativity and efficiency. They’re particularly notable for their ability to democratize content creation, enabling people without specific skills (like coding or graphic design) to create complex work or assist in the development process. Here’s an overview of the various types available.
These platforms are specially designed to generate synthetic data that can be used for training machine learning models where real data might be scarce, sensitive, or biased.
A GenAI application builder is a cutting-edge platform that streamlines the software development process. This intuitive system aids users in crafting, evolving, and launching applications by automating the generation of code, interfaces, and content, all keyed off simple user directives.
Using natural language descriptions or user-friendly graphical interfaces, the core functions of these builders can dramatically reduce the complexity of app creation. Here are nuances of their operation, as well as their standout features.
Application builders combine general generative AI features, functions, and capabilities with those designed specifically for application development:
Generative AI application builders speed up development by handing routine and complex tasks to AI, so teams can move from idea to working app faster. They also make app building more accessible for people without a deep coding background, which helps more teams participate in software creation. Less manual hand-coding can lower costs for small businesses building custom apps and for large enterprises racing to meet deadlines. With the busywork off the table, builders can spend more time on standout features and better user experiences. And while much of the process is automated, these tools still support customization and scalability, so you can adjust generated code or layouts to fit specific requirements and performance needs.
In a 2023 survey, Microsoft found that 87% of CEOs believe that increased AI and automation embedded into low-code platforms would help them better use the full set of the technology's capabilities.
OutSystems is a leading AI development platform that helps teams build, deploy, and manage AI apps and agents across the full development lifecycle. It empowers developers to get more done and ship faster by reducing mundane, repetitive hand coding and lifecycle management tasks, and it provides numerous ways to incorporate generative AI into applications.
With OutSystems’ AI-powered approach, developers can add GenAI capabilities to projects with speed and ease. Its intuitive drag-and-drop experience also makes it possible to create and deploy GenAI apps without requiring AI-specialized knowledge.
OutSystems also offers a wide range of pre-built connectors, quick-start generative AI apps, and templates designed to accelerate AI development. These ready-to-use resources enable developers to automate tasks and unlock new levels of productivity and innovation.
For developers moving into generative AI, OutSystems provides robust support and documentation. Its extensive knowledge base, tutorials, and community forums help ensure developers have the resources they need to successfully leverage generative AI within their applications.
Developers can unlock the potential of generative AI using the OutSystems AI development platform without getting bogged down in complex technical challenges. The platform’s seamless integration capabilities, pre-built components, and comprehensive support make it an ideal choice for teams seeking to harness the power of GenAI easily.
