Developing a generative AI app isn’t as easy as using one – but it’s getting closer.
Many vendors offer to help you tackle one part or another in the process, but that still leaves many moving parts to contend with before you can successfully launch an application. Here’s a look at what you need to know to get from start to finish – one moving part at a time.
Early on, everyone experimented with brand-new, public generative AI apps. It was exciting to see what this class of AI could do. It was also a heart-pumping experience to discover how easily the experimentations could turn into security issues. Examples ranged from a ChatGPT data breach that allowed some users to see other users’ information to prompt injections and employees entering proprietary data that became model training fodder.
It quickly became clear that generic models were not ideally suited for corporate use. Organizations needed a way to add security, flexibility, and data to fully leverage the capabilities of this new and market-changing technology.
GenAI vendors responded with business and enterprise versions of generic models and APIs to aid developers in integrating with other applications and data sources. Developers could also customize generative AI web applications as needed.
While these product advancements have been universally welcomed, integrations are still challenging. Fortunately, there are some accelerators, like MeteorAI, Caylent’s generative AI framework, that can make integrations easier and faster. For example, MeteorAI supports numerous use cases, including the development of AI assistants, enterprise knowledge bases, forecasting, recommendation engines, anomaly detection, pattern recognition, and data generation.
Here’s a look at the key features you need to consider when developing a generative AI application – and how the right framework can speed up adoption.
API-first design
Quick and ready connections to other systems allow you to easily add data sources within the platform, enabling fast and easy integrations. A strong generative AI framework like MeteorAI provides plenty of prebuilt connectors, creating a platform that scales with demand and quickly responds to market trends.
Cloud integrations
Integrating generative AI applications with existing cloud services and workloads can be very difficult due to issues with compatibility, scalability, security, and data transfer. Cloud-native frameworks speed up the integration process and eliminate many deployment issues. For example, AWS native frameworks, such as MeteorAI, have built-in, secure AWS products and tools to ease integrations with AWS services. Plus, familiarity with AWS tools means your developer team doesn’t have to spend time learning new ones.
Train and build custom models with your data
Generative AI models perform better when applications are specialized rather than used for general purposes because of the lack of domain-specific knowledge. A robust framework will train and customize various models to perform better in applications purposely formed to address a specific use case, resulting in more relevant and high-quality outputs.
Multi-model support
Inevitably, organizations find themselves working with more than one AI model. Managing and integrating multiple AIs, data models, and AI applications can present numerous challenges and conflicts. Having a centralized platform allows you to effortlessly integrate future models (e.g. GPT4, Claude, Llama, Anthropic, and more) and streamline the management process for monitoring, deploying, and updating multiple AI models.
While developing a generative AI app may not be as effortless as utilizing one, the ongoing advancements in technology are steadily bridging the gap between user simplicity and development complexity.
Learn how to fast-track your generative AI aspirations with MeteorAI by Caylent.