Finding the use case, data strategy and integration into your applications and processes.
ChatGPT and other Generative AI applications have sparked the imagination of product teams across all industries. A holistic strategy to leverage Generative AI at scale needs the right use case and a data strategy that takes into account the associated IP and security concerns.
The recent wave of generative AI has been enabled by the scale and ability of cloud providers, the increased volume of data and remarkable breakthroughs in transformer networks. To leverage these capabilities at scale a Cloud-First approach to host and scale these models is needed. Services like AWS Bedrock allow the models to expand and Serverless allows the models to scale. Building a scalable hosting and API interface, while embedding the user interfaces needed to leverage LLMs requires advanced cloud knowledge, innovative UI/UX approaches and a strong focus on data compliance and security.
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Use Case Identification
While there is huge investment and attention on Generative AI, we're just scratching the surface of the impact to day-to-day business. Identifying the correct use cases in your application and organisation requires a hands on knowledge of the possible, experience leveraging GenAI in other companies / industries and the ability to take a step back from the status quo.
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Data Strategy
The power, quality and risk in Gen AI applications start with the data. Across the generative AI supply chain there are risks to IP Infringement, security and access control stemming from training, selecting foundational models, engineering prompts, context, hosting, fine tuning, and integration.
Outside of the supply chain risks, having a rich data lake across business process and key interactions is key to fully leverage the power of GenAI. By integrating with legacy systems and building an AI-First data strategy, companies can set themselves up to leverage the power of AI today, and the future of AI tomorrow.
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Model Hosting and Integration
To enable internal and third party applications to leverage the capabilities of LLMs (e.g. text generation, chatbots, image generation, search and more) an API layer needs to be built. Fully Serverless APIs provide the scale needed while integrating natively with Cloud LLM services. Websocket connections can be crucial for low latency chat interfaces, and Serverless services like AWS's AppSync can provide these with no management overhead. Existing foundational models are customized with propriety application data to train powerful domain specific models. These are then be hosted at scale using Cloud-Native and Serverless services (e.g. AWS Bedrock, SageMaker and others).
In addition fine tuning and prompt engineering may be needed to achieve the required end result / use case. Once an API Layer has been established it’s key to ensure compliance and security with an integrated authentication and logging strategy. Innovative and extremely dynamic user interface components are then built and integrated to provide the end-to-end LLM application capability.
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