// Abstract

The popularity of ChatGPT has brought large language model (LLM) apps and their supporting technologies to the forefront. One of the supporting technologies is vector databases. Yujian shares how vector databases like Milvus are used in production and how they solve one of the biggest problems in LLM app building - data issues. They also discuss how Zilliz is democratizing vector databases through education, expanding access to technologies, and technical evangelism.

// Bio

Yujian Tang is a Developer Advocate at Zilliz. He has a background as a software engineer working on AutoML at Amazon. Yujian studied Computer Science, Statistics, and Neuroscience with research papers published to conferences including IEEE Big Data. He enjoys drinking bubble tea, spending time with family, and being near water.

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Timestamps:

[00:00] Yujian's preferred coffee

[02:40] Takeaways

[05:14] Please share this episode with your friends!

[06:39] Vector databases trajectory

[09:00] 2 start-up companies created by Yujian

[09:39] Uninitiated Vector Databases

[12:20] Vector Databases trade-off

[14:16] Difficulties in training LLMs

[23:30] Enterprise use cases

[27:38] Process/rules not to use LLMs unless necessary

[32:14] Setting up returns

[33:13] When not to use Vector Databases

[35:30] Elastic search

[36:07] Generative AI apps common pitfalls

[39:35] Knowing your data

[41:50] Milvus

[48:28] Actual Enterprise use cases

[49:32] Horror stories

[50:31] Data mesh

[51:06] GPTCash

[52:10] Shout out to the Seattle Community!

[53:44] Wrap up