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-Domain-Driven LLM Development: Insights into RAG and Fine- Tuning Practices
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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.
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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.
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