From 6fe3545e0f8c54072f825afa4fd929ae3c04abb8 Mon Sep 17 00:00:00 2001 From: Yunfei Bai Date: Sun, 18 Aug 2024 21:46:41 -0700 Subject: [PATCH] Update index.html --- index.html | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/index.html b/index.html index b5d5f7f..3f73866 100644 --- a/index.html +++ b/index.html @@ -1,4 +1,9 @@ -Domain-Driven LLM Development: Insights into RAG and Fine- Tuning Practices + + + + Domain-Driven LLM Development: Insights into RAG and Fine- Tuning Practices + + Abstract To improve Large Language Model (LLM) performance on domain specific applications, ML developers often leverage Retrieval Augmented Generation (RAG) and LLM Fine-Tuning. RAG extends the capabilities of LLMs to specific domains or an organization's internal knowledge base, without the need to retrain the model. On the other hand, Fine-Tuning approach updates LLM weights with domain-specific data to improve performance on specific tasks. The fine-tuned model is particularly effective to systematically learn new comprehensive knowledge in a specific domain that is not covered by the LLM pre-training. This tutorial walks through the RAG and Fine-Tuning techniques, discusses the insights of their advantages and limitations, and provides best practices of adopting the methodologies for the LLM tasks anduse cases. The hands-on labs demonstrate the advanced techniques to optimize the RAG and fine-tuned LLM architecture that handles domain specific LLM tasks. The labs in the tutorial are designed by using a set of open-source python libraries to implement the RAG and fine-tuned LLM architecture. @@ -17,3 +22,6 @@ The Co-Founder and CEO of Epsilla Inc, a one- stop RAGaaS platform for building production ready LLM applications. With a background in big data, vector graph databases, and high performance computing, Richard helps customers build production-ready RAG systems connected with large scale proprietary data. Richard holds a Master’s degree in Computer Science from Cornell University. Yunfei Bai A Senior Solutions Architect at Amazon Web Services. With over 15 years’ experience on AI/ML, Data Science and Analytics, Yunfei helps AWS customers adopt AI/ML and Generative AI services to deliver business results. Prior to AWS, he worked in various roles including product manager and solution consultant in multiple industries, designed and delivered AI/ML and data analytics solutions that overcome complex technical challenges and drive strategic objectives. Yunfei has a PhD in Electronic and Electrical Engineering. He has published research papers and blog posts, and serves as a journal reviewer. + + +