Unstructured Data in LLMs

Topic: Connecting your unstructured data with LLMs

What we’ll do:
Have some food and refreshments. Hear three exciting talks about LLMs and unstructured data.

5:30 - 6:00 - Welcome/Networking/Registration
6:05 - 6:30 - Rachel Hu, Founder, CambioML
6:35 - 7:00 - David Garnitz, Founder, VectorFlow
7:05 - 7:30 - Yi Ding, Developer Advocate, LlamaIndex
7:30 - 8:30 - Networking

Who Should attend:
Anyone interested in talking and learning about Unstructured Data and LLM Apps.

When:
September 27th, 2023
5:30PM

Where:
This is an in-person event! Register to see the location.
Sponsored by Zilliz

Tech Talk 1: Advanced Strategies for Production RAG
Speaker: Yi Ding, Developer Advocate, LlamaIndex
Abstract: As we pass the second anniversary of ChatGPT's knowledge cutoff, it's clearer than ever that retrieving the right data for the LLM is crucial for the majority of applications. What we've seen at LlamaIndex is that once you get past the demo, tuning your RAG strategy involves a lot of domain specific optimization and tradeoffs. I will be discussing a number of more advanced strategies including SubQuestionQueryEngine, "Small to Big," Document Hierarchies, and Milvus's Hybrid Search.

Tech Talk 2: Ingesting Chaos: Handling Unstructured Data Reliably at Scale for RAG & Beyond
Speaker: David Garnitz, Founder, VectorFlow
Abstract: The wide range of scenario and edge cases to account for makes ingesting and processing unstructured data into vector databases difficult. You can offload some of the complexity by using a vector embedding pipeline. This, in combination with an automated evaluation system, will allow you to experiment with different ingestion techniques to see what works best for your data and use-case.