The Evolution of Generative AI Product Architecture

This article is a collaborative piece between Matteo Castiello and Igor Nikolaienko - AI Solution Architect.

Developing a generative AI product often involves navigating uncharted waters. There is no commonly accepted, universal methodology of how to plan, test, develop and deploy applications for different use cases and technology architectures.

Nevertheless, there is a plethora of established best practices from the existing theory on the product development lifecycle. In combination with this existing theory and the newly emerging generative AI tech stack, we have developed an approach that provides a structured and phased framework that can be tailored to the unique challenges and opportunities of Generative AI product development.

The Approach

Stages and Layers

The Evolution of Generative AI Product Architecture outlines the progression through key stages and the importance of layers. The stages mirror the journey from a basic functional setup to a sophisticated, market-ready system. Simultaneously, the layers of architecture are inter-elated architectural that components play a role in building a robust and adaptable AI product.

“It’s so early, most of what I see coming right now is still in the foundational/base model area. It’s not ok, how we do we use this all the way up the stack?” - Dylan Field - CEO Figma

Building an AI product can be paralleled with constructing a house, where each layer represents a critical component that can be swapped or upgraded, just like the interchangeable modules of a home.

Stages

Generative AI product development follows a structured journey through three key stages: Proof of Concept, Minimum Viable Product, and Production. This phased approach facilitates the gradual evolution from a basic, functional model to a fully-integrated, market-ready application.

?? Stage 1 - Proof of Concept (POC) or Prototype: This is like renting a home. It's functional and allows you to quickly test the liveability of the space without a long-term commitment. You use off-the-shelf tools and furnishings to see if the house meets your basic needs. It's not custom-built, but it's a starting point to validate your requirements for a comfortable home.

?? Stage 2 - Minimum Viable Product (MVP) or Pilot: Now you're purchasing a plot and building a modular home. It's more personalized than the rental and has room for additions, but it’s still using pre-made components for efficiency and cost-effectiveness. This is your MVP – a product that's beyond the conceptual stage and is functional enough to show to stakeholders and early adopters, giving them a feel for the final living experience.

??? Stage 3 - Production: At this stage, you're constructing a fully customized home from the ground up. You've laid a solid foundation, chosen durable materials, and are adding unique features that make the house robust and welcoming. For your AI product, this translates to a sophisticated, scalable system designed to fully integrate into and enhance the infrastructure of the user's environment. It's no longer just a structure; it's a well-engineered home that's ready for the market and can withstand the demands of everyday use.

Layers

The architecture of a generative AI product is built upon six integral layers: Data, Model, Security, Integration, Control, and User Interface. Each layer serves a unique function, contributing to the product's overall efficiency, security, and user experience.

????Data Layer: Consider this the foundation of our modular home. It's composed of the data files that are the bedrock of the AI, setting the stage for a scalable and integrable system that will evolve to an interface with databases, APIs, and cloud services as it grows.

?? Model Layer: Think of this as the structural framework of our home. Here, off-the-shelf Large Language Models (LLMs) form the initial structure. As the product progresses through the stages, models are honed and fine-tuned, reinforcing the product, much like how we enhance the walls of a house for greater stability.

?? Security Layer: This is the home's security apparatus. Starting with standard off-the-shelf options and maturing into complex, enterprise-grade licenses and policies, this layer is dedicated to ensuring regulatory compliance and safeguarding the integrity of the AI system, upgrading security measures as needed.

?? Integration Layer: Like the utilities of a house, the integration layer ensures seamless operation and communication between data sources and systems. Starting with basic off-the-shelf components, it moves towards a more refined orchestration, employing CI/CD pipelines for continuous improvement and deployment.

??? Control Layer: This critical layer represents a smart house's control panel, measuring lighting, heating, cooling, and overall efficiency of the home. Similarly, a generative AI application must employ controls on cost and eventually governance mechanisms and continuous monitoring. This is similar to upgrading from a manual thermostat to a smart home management system that optimizes for efficiency and sustainability.

?? User Interface Layer: The facade of the house, including doors, windows, and the overall aesthetics. It's the user's first point of contact with the AI, necessitating a design that's both functional and appealing, evolving from basic interfaces to mature, sophisticated applications.

“A shared playbook is developing as companies figure out the path to enduring value. We now have shared techniques to make models useful, as well as emerging UI paradigms that will shape generative AI’s second act.” - Sequoia Capital

Considerations

The Evolution of Generative AI Product Architecture highlights the inter-relation of each stage and layer and considers that as an AI product matures, the architecture remains fit-for-purpose, flexible, and capable of consuming enhancements over time. This component of flexibility and modularity allows the reflection of an enterprise's objectives but also allows an organization to incrementally consume innovation as its business is ready for it.

The aforementioned framework is a general approach and does not consider specific use cases. Tailoring this framework based on business type, industry, or function is necessary. For example, a regulated organization with sensitive data might require on-premise model fine-tuning or the implementation of sophisticated security measures in the early stages of product development.

This representation is designed as a standard approach where an organization is dealing with unstructured textual data, offering a foundational understanding that can be adapted and scaled according to the unique demands and complexities of individual AI projects.

For those that are considering their first or their next generative AI use case, we have developed an "AI Architect Advisor". This is a custom GPT model tailored to provide expert consultancy for AI product development. We encourage you to engage with this tool and explore its capabilities. Your feedback is crucial, as it will drive further enhancements and ensure that our tool remains at the forefront of generative AI architecture strategy and implementation.