📣 Webinar: Learn how to fine-tune RAG and boost your content quality with Zilliz and 🔭 Galileo

If you’re trying to improve the quality of your LLM-generated responses, you’ve probably explored retrieval augmented generation (RAG). Grounding your model on external sources of information improves explainability and the quality of responses. To truly make the most of RAG, you need the right infrastructure and evaluation frameworks in place. 

Join Vikram Chatterji of Galileo and Yujian Tang of Zilliz for a deep dive into RAG and LLM management. This webinar will equip you with actionable insights and methods to enhance your LLM pipelines and output quality.

During this hands-on workshop, you’ll learn: 

• When you should do fine-tuning for RAG

• When you should use a vector database for RAG

• The architecture of Zilliz's vector database and its role in RAG

• Mechanisms for fine-tuning language models to generate precise outputs

• A metric-driven framework for evaluating RAG context sparsity and relevance

• Techniques to ‘fix’ content sparsity and improve LLM outputs based on quantitative evaluation

This promises to be an informative session for any data scientists and machine learning engineers looking for frameworks and tools to optimize RAG and LLM performance. 

Date/Time: Oct 12, 2023, 09:00 AM Pacific