Keynote: Unlocking Corporate Data with Retrieval-Augmented Generation
summary
A practical roadmap for moving beyond basic RAG: use LlamaIndex to connect drives, PDFs, databases and APIs to an LLM (quickstart in five lines of code) but expect naive RAG to work only for single-fact QA and fail on summarization, comparisons, structured SQL queries and multi‑step tasks. Add an agent reasoning layer (example: ReAct) to decompose queries, call retrieval tools and run SQL, and try the open‑source SEC Insights template (MIT) as a production example.
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chapters (9)
- 0:00Introduction
- 0:43LlamaIndex and RAG
- 2:28Naive RAG limitations
- 4:11Agents for complex queries
- 5:36Agents enhance RAG
- 7:04Agent-based RAG components
- 7:47Financial analysis with RAG
- 8:50Enterprise RAG resources
- 10:16Call to action
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