[Introduction to Retrieval Augmented Generation (RAG) with Google Gemini]

Have you ever encountered Generative AI provide answers that are factually incorrect or illogical? 🗨This is because Large-Language Models (LLM) alone is not sufficient due to inherent challenges that impact its effectiveness in providing accurate and reliable information. 💡Here is where we need Retrieval Augmented Generation (RAG) to fill in the gap of LLM. RAG is the process of optimising the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. ✔

✨Get a glimpse into the future of RAG with Dr. Poo Kuan Hoong, Lead Data Scientist, BAT

Google Developer Expert (Machine Learning). ✨

Excited to understand how scientists are enhancing Generative AI? 💭 Learn how RAG plays a crucial role in delivering responses that are more accurate, relevant and original while also sounding like they came from humans! Join us on the intriguing talk on the basic concepts of RAG and how they improve our daily lives. Not to miss out key components, all the way from the operation of RAG, conversational agents, legal research and analysis, and even how it will help your assignment and homework! 🤯

🎤 Featuring:

Dr. Poo Kuan Hoong

Lead Data Scientist, BAT,

Google Developer Expert (Machine Learning).

Keep your calendar marked!

📅 Date: 24th May 2024, Friday

⌛ Time: 02:30 PM (MYT)

🏛 Venue: Google Meet

📌 UTM merit points will be provided

📌 Open for both students and staffs

🔗 RSVP Link:

https://gdsc.community.dev/e/mjrncv/

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https://chat.whatsapp.com/CwHShP2KPHBAEzOfj9YYfA

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