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Generative AI (Gen AI) and large language models (LLM) are awe-inspiring technologies getting ready to take their places in future-shaping tools of the highest order. Innovations like ChatGPT, Azure OpenAI, and Google’s Gemini will revolutionize how the digital world we live in looks and feels within a very short time. From Gen AI’s ability to “understand” and create text, create visual media, and even full applications, we need to be ready to embrace Gen AI capabilities.
OutSystems, a low-code enterprise application development platform, is adopting these technologies to allow developers to create applications up to 100x faster than traditional developer. Yes, you read that right. Using much the same prompt-writing philosophy behind other Gen AI-based media, platforms like OutSystems can generate quality enterprise applications for internal and external use alike, tailored to your specified inputs. Your organization stands to benefit immensely from faster time-to-market, tighter security, and the kind of forward-thinking customer experience that the leaders in retail, BFSI, and other industries are known for.
Before we jump into application generation though, let’s take a moment to understand the building blocks of Gen AI, starting with Large Language Models (LLMs). LLMs are custom built, finely tuned models for generating and classifying text. ChatGPT, for example, have developed a few of these: gpt-3.5-turbo, gpt-4.0, and the new gpt-4o. The most enterprising tech minds in may find it tempting to bring everything in-house and create their own proprietary LLMs, but would that necessarily be a good idea?
Are proprietary LLMs worth building in-house?
As game-changing as they are, the major Gen AI offerings are works in progress that have cleared massive hurdles just to get to where they’re at now. The tech industry as a whole has learned some key lessons from the establishment of Gen AI:
- Gen AI and LLMs are billion-dollar investments that take time to perfect
- Building these models requires mass amounts of data collection, preparation, and compute power
- High-level computational architecture and infrastructure must be in place to run it
- Maintaining ethics while avoiding bias concerns and IP infringement must be ensured
Developing a market-worthy Gen AI or LLM setup requires significant resources in terms of financial investment, working hours, and computational power. Were you to start gathering the required funds, personnel, and expertise immediately after reading this article, you would still be years behind the work that the likes of OpenAI, Google, and Meta have already put in.
There’s no way around it - building effective AI models requires specialized knowledge in parallel innovations like machine learning and natural language processing. What’s more, these models are trained on mass amounts of data, and there’s no guarantee that the existing data relevant to your use case will be readily available to you. Even if you should find an extensive sample of data suited to your tool concept’s purposes, there remains the arduous work of separating the wheat from the chaff, refining the data to make sure it minimizes or eliminates privacy concerns, ethical concerns, demographic bias, and intellectual property infringement. Though companies have access to data, they may not have the license or expertise to curate and preprocess it with the same effectiveness as the leaders in the space.
Conversely, while these Gen AI endeavors from OpenAI, Google, and Meta are widely used for their generative prowess, their models have not been perfected. These companies are currently contending with lessening the adverse impacts (both potential and foreseeable) brought on by the sudden, widespread global use of these groundbreaking tools. The challenges outlined above, which are faced by the biggest names in Gen AI, even with their talent and funding, have a high likelihood of sinking your organization’s own tool-building efforts.
Now that we’ve covered a bit about LLMs and their current state, how do we translate that into building applications with OutSystems?
OutSystems Combines the Powers of GenAI and Low-Code to Work For You
OutSystems has worked to adopt Gen AI in two different aspects: 1 - using Gen AI to generate OutSystems applications from prompts (Morpheus), and 2 – integrating Gen AI capabilities into your low-code applications to enhance your customer experience.
Morpheus is a visionary spectacle of technology, built on OutSystems ODC – OutSystems Developer Cloud. Using Gen AI powered low-code technology can help you generate enterprise-level apps with the functionalities you need to grow your business or offer your clientele services they didn’t know they needed.
> See here for more on Morpheus: https://www.outsystems.com/news/generative-ai-roadmap/
In addition to Morpheus, OutSystems is a general purpose low-code development platform that enables organizations to rapidly build, deploy, and manage web and mobile applications, providing a visual app-building experience that allows developers to create applications quickly, with minimal hand-coding, and to exact specifications. Creating low-code apps with OutSystems means your business stands to benefit from:
- Speedy development
- Integration with third-party APIs and apps such as Microsoft Azure OpenAI
- Easier scalability
- Decreased maintenance
- Increased security
OutSystems has made significant investments in extending their low-code platform to easily integrate with Gen AI solutions. This includes:
- Prebuilt integrations into Azure OpenAI
- AI Agent Builder – an OutSystems solution that makes it easy to create AI based agents built in to your own applications, for call centers, translations, virtual assistants, and more
> See here for more on AI Agent Builder - https://www.outsystems.com/low-code-platform/gen-ai/
All of that said, OutSystems already allows you to easily extend your applications using Gen AI for a great customer experience, and you can learn more in our webinar!
Get In The Know With Xebia’s Webinar on Gen AI and OutSystems
If the benefits of Gen AI and Low-Code seem too good to be true, then a peek behind the curtain into its inner workings will help you understand the potential they hold for your team, business, or organization. Two of Xebia’s brightest Gen AI minds, Senior Data Science Consultant Katherine Munro and Chief Technology Architect Craig St. Jean, have put together an illustrative presentation on the ins-and-outs of Gen AI and LLMs, as well as a granular analysis of how OutSystems integrates into technology stacks and creates bespoke applications. It’s all in our webinar, which you can click below to watch now.
