RAG is a hack - with Jerry Liu of LlamaIndex

Latent Space · Beginner ·🧠 Large Language Models ·2y ago
In October 2022, Robust Intelligence hosted an internal hackathon to play around with LLMs which led to the creation of two of the most important AI Engineering tools: LangChain 🦜⛓️ and LlamaIndex 🦙 by Jerry Liu, which we’ll cover today. In less than a year, LlamaIndex has crossed 600,000 monthly downloads, raised $8.5M from Greylock, has a fast growing open source community that contributes to LlamaHub, and it doesn’t seem to be slowing down. Full show notes and transcript here: https://www.latent.space/p/llamaindex#details 00:00:00 Introductions and Jerry’s background 00:04:38 Starting …
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Chapters (23)

Introductions and Jerry’s background
4:38 Starting LlamaIndex as a side project
5:27 Evolution from tree-index to current LlamaIndex and LlamaHub architecture
11:35 Deciding to leave Robust to start the LlamaIndex company and raising funding
21:37 Context window size and information capacity for LLMs
23:09 Minimum viable context and maximum context for RAG
24:27 Fine-tuning vs RAG - current limitations and future potential
25:29 RAG as a hack but good hack for now
28:09 RAG benefits - transparency and access control
29:40 Potential for fine-tuning to take over some RAG capabilities
32:05 Baking everything into an end-to-end trained LLM
35:39 Similarities between iterating on ML models and LLM apps
37:06 Modularity and customization options in LlamaIndex: data loading, retrieval, syn
43:10 Evaluating and optimizing each component of Lama Index system
49:13 Building retrieval benchmarks to evaluate RAG
50:38 SEC Insights - open source full stack LLM app using LlamaIndex
53:07 Enterprise platform to complement LlamaIndex open source
54:33 Community contributions for LlamaHub data loaders
57:21 LLM engine usage - majority OpenAI but options expanding
1:00:43 Vector store landscape
1:04:33 Exploring relationships and graphs within data
1:08:29 Additional complexity of evaluating agent loops
1:09:20 Lightning Round

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