Journey inside Uber's innovative AI assistant "Genie" with Paarth Chotani, Staff Engineer at Uber, as he shares how they're revolutionizing on-call support using LLMs and vector search. From processing massive amounts of internal documentation to building scalable RAG pipelines, discover how Uber tackles the challenges of implementing AI assistants at scale. Get insights into the evolution from traditional chatbots to agent-based solutions, and learn practical lessons about staying current in the rapidly evolving AI landscape. Whether you're building AI-powered tools or scaling data infrastructure, this episode offers valuable perspectives on balancing innovation with real-world implementation.

• Building and scaling RAG pipelines at enterprise scale

• Evolution from traditional chatbots to AI agents

• Practical insights on data processing and vector search implementation

• Leveraging open-source technologies in production environments

• Navigating rapid technological changes in AI development

Paarth is a Staff Engineer at Uber, where he works on Michelangelo, Uber's machine learning platform. With over four years at Uber, he specializes in feature store development, online serving at scale, and GenAI implementations. He has been instrumental in developing Genie, an AI-powered on-call assistant that revolutionizes how Uber's engineering teams handle support requests and documentation access. In this episode, Paarth shares valuable insights on building and scaling RAG-based systems, vector search implementations, and the evolution of AI assistants from traditional chatbots to sophisticated agent-based solutions. His experience spanning both AWS chatbot development and current GenAI innovations at Uber offers listeners a unique perspective on the rapid advancement of AI-powered enterprise solutions.

"Think of Genie as your on-call assistant. Different infra teams have their Slack channels, and because these technologies are widely used, you have to wait a lot." - Paarth

"What we realized is for our engineers to really get help, data sources really should be internal only because we customize lot of these open source engines for making it work at Uber scale." - Paarth

"Instead of building a mega scale pipeline that just ingest all data sources and then keeps a central data source solution, we instead are giving users the flexibility to ingest what data sources they want." - Paarth

"We had to scale our you can say the whole infrared layer to chunk data faster to be able to create embedding set scale." - Paarth

"It almost felt like they're doing what EMR was doing. You have your Hadoop and big data technology, and we needed these pipelines to basically process all this data quickly." - Paarth

"We've even evolved from just giving you the right documentation to starting to evolve into a situation where we'll also start taking actions on your behalf." - Paarth

"That intuition that comes from building this kind of bot, I feel like that intuition came again as we were starting to see this technology come, and we're like, hey, this looks like where you can pretty much fit all these pieces together." - Paarth

"What we have seen with several use cases is agentic genie works well when designed well, when you've analyzed the problem of which type of subproblems the bot should resolve per channel, per use case." - Paarth

"I think having a problem in mind always helps that way, the energy is little bit focused and directed." - Paarth

"Whatever you're building is not enough because the expectation has already gone to the next level, so the pace is too fast right now." - Paarth