Come see a demo-heavy exploration into application architecture and design patterns that leverage Large Language Models (LLMs), transforming application design, development, and deployment.

The session begins by showing the foundational technique of leveraging LLMs through direct invocation. This part focuses on the art of prompt engineering, crucial for tailoring LLM responses to the nuanced needs of applications.

Following this, the session talks about bridging natural language queries into structured queries. This demonstrates how to enable powerful searches across both structured and semi-structured data repositories, by using LLMs to interpret and translate human queries into actionable database commands.

Next, the session looks at the vector databases and how these work, allowing for nuanced searches that go beyond keyword matching and mere Boolean operations.

All of these lead up to RAG apps -- A style of application that creates powerful apps for searching and analyzing vast quantities of data in natural language.

The final part of the session looks at how LLMs work with other AI models. This shows how layering AI technologies to achieve a depth of interaction and functionality previously unattainable with single-model approaches, building applications that are richer and more engaging.