Since ChatGPT rocketed the potential of generative AI into the collective consciousness, there has been a race to add AI to everything. Product owners are excited by the possibilities of new AI-powered features, and marketing departments are eager to showcase “powered by AI” capabilities. For the average layperson, ChatGPT's conversational interface might seem simple, but integrating these tools securely, reliably, and cost-effectively requires much more than just adding a chat interface. Moreover, achieving consistent results with these tools involves both technical and creative considerations. Serious integration of AI tools into applications demands a thoughtful approach.

This session is not about “AI is Magic” cheerleading nor a critical dissection of the field. Instead, it focuses on valid use cases for AI tools and introduces architecture patterns for implementing them. We’ll explore trade-offs of various patterns and the application of AI in each scenario, ranging from simple, direct integrations to more complex architectures involving Retrieval-Augmented Generation (RAG) and managing multiple fine-tuned models. The content is practical and based on real-world production experiences, not just theoretical concepts.

Attendees will leave with insights into how to approach and manage AI-powered projects, ensuring they are prepared for future AI-driven initiatives.

Target Audience: This session is primarily aimed at DeepTech Professionals, as it delves into advanced AI integration strategies and architectural patterns, essential for those working on AI-powered applications. Software Architects are the secondary audience, as they need to understand how to design systems that securely and efficiently incorporate AI features. Back-end Developers will also find value in learning how to implement and manage AI components within existing architectures, ensuring scalability and reliability in AI integrations.