In this workshop, attendees will delve deep into the world of Large Language Models (LLMs), Retrieval-augmented Generation (RAG), and their significant impact on real-world applications, exemplified by open source project QuickTrace.

Session Breakdown:
1. Large Language Models (LLMs):
Comprehensive insights into the architecture and functionality of LLMs.
Hands-on exercises and discussions on renowned models like Google’s Bard, Meta’s LLAMA and OpenAI’s ChatGPT.
2. Retrieval-augmented Generation (RAG):
Introduction and practical application of RAG for optimizing LLMs.
Interactive sessions to experience RAG’s enhancement of AI-based software.
3. QuickTrace - A Real-world open source Application:
Detailed exploration of the development and success of QuickTrace, our award-winning open-source project.
Insights into challenges, strategies, and innovations in AI-driven software development.
Key Learning Outcomes:
Foundational Knowledge: Gain a solid understanding of LLMs and prominent models like Bard, LLama and ChatGPT.
Practical Skills: Acquire hands-on experience in implementing RAG to optimize LLMs.
Real-world Insights: Explore the development and success of QuickTrace, offering a tangible perspective on AI application.
Interactive Learning: Engage in discussions and networking opportunities with peers and field experts.
Join Us!
Embark on a journey where theoretical knowledge meets practical application, and innovative ideas meet real-world execution. Equip yourself with the insights and skills to not only comprehend AI and LLMs but to adeptly apply them in developing impactful, real-world AI solutions.