Jerry Liu on the Future of AI: LlamaIndex, LLMs, RAG, Prompting and more

In this 25th episode, Jerry, co-founder and CEO of LlamaIndex shares valuable insights for anyone implementing LLM solutions. I decided to focus on Retrieval Augmented Generation (RAG) since it is the current "hype" solution, but we also touched on LLMs in general, the importance of good communication and documentation and more about the business sides of things. I hope you enjoy this episode of the What's AI podcast!
Listen on Spotify: open.spotify.com/episode/6kwA...
Listen on Apple Podcasts: podcasts.apple.com/ca/podcast...
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Chapters:
0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.
01:30 Jerry shares about his journey into AI
03:29 Went into AI product space instead of doing a master's degree and research?
04:56 Understanding the Basics of AI and LLMs
07:16 Beginner in AI... note down this recommendation!
09:50 Understanding LlamaIndex
12:40 Comparing LlamaIndex with Other Tools
16:30 Thoughts on Gemini?
19:29 Dealing with Multimodalities in LlamaIndex
21:25 Importance of Good Documentation
24:12 Deep Dive into Retrieval Augmented Generation (RAG)
28:35 RAG vs Fine-Tuning
34:43 The Importance of Chunking Size in RAG System
37:49 The Importance of Data Quality in Chunks
42:55 Evaluate the RAG system
46:05 The future of LLM
49:29 The Role of Prompt Engineering in AI
53:33 Which AI tools do you use?
57:11 About Llamaindex
#ai #llm #rag

[@paulntalo1425]

Thank you for sharing

[@ramp2011]

Awesome interview. I also truly appreciate the effort interviewer has taken to create very thoughful and engaging questions. thank you....

[@WhatsAI]

Thanks a lot! I really appreciate that it was seen 😊❤️

[@DD-ds7ui]

thanks!

[@sudhamabhatia6322]

hi Louis, loved this interview! you brought out some great answers. Jerry mentioned a bunch of tools they use or generally useful for things like reading PDF's. Can you please list links to these tools in the description of the video?

[@horyekhunley]

Why shouldn't you use RAG with csv data?

[@WhatsAI]

You can do that!