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Generative AI: Comparing DSF and RAG techniques

Tianjun Zhang

Tianjun Zhang

PhD Student at Berkeley RISE Lab

Generative AI: Optimizing Natural Language Interfaces

In the realm of digital innovation, effective data use through generative AI is not just an advantage but a necessity. Businesses are continually seeking innovative solutions to enhance their operations. This technology is transformative, but how can it be best utilized to optimize natural language interfaces across various sectors?

Join us for an in-depth exploration of the advanced methodologies that are setting new standards in AI applications: Domain-Specific Fine tuning (DSF) and Retriever-Augmented Generation (RAG). Throughout the webinar, we will engage in a thorough discussion on the nuances of both DSF and RAG. Learn about the practical implications of these technologies through real-world examples and case studies that demonstrate their significant impact on business efficiency and responsiveness. Additionally, we will delve into the RAFT technique, a pivotal development that prepares your large language models (LLMs) for more robust, open-book operations.

 

What you will learn:

  • Overview of Generative AI Applications: Introduction to how generative AI is being integrated into sectors like banking, legal, and medical to streamline operations and improve service delivery.
  • Exploring DSF and RAG: Detailed examination of the two primary methods for implementing generative AI, focusing on their functions, benefits, and optimal use cases.
  • Latest Advances in AI Technology: Insight into cutting-edge techniques such as RAFT and comparisons of Llama2-7B with state-of-the-art models like GPT-4.

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