Beyond LLMs and Maturing RAG use cases
summary
Claims that vanilla LLMs solve reasoning fail on simple tests — a model spending 19 seconds still answers “strawberry” R‑count wrong (says three Rs), and a student Fortran experiment shows ChatGPT teams deliver code faster but retain less understanding. The platform Vectara applies RAG with Boomerang retrieval and Slingshot ranking plus a tuned LLM Mockingbird, adds a hallucination evaluation model that outputs a monitorable score, and a query‑intelligence automatic filter that reliably surfaces incidents by ID.
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chapters (14)
- 0:00Introduction
- 1:11LLMs and RAG limits
- 5:25Vectara RAG platform
- 12:46Maturing RAG use cases
- 15:13Question alignment
- 17:18Data trustworthiness
- 19:26Corpus modeling
- 21:30Database structuring
- 23:15Document processing
- 25:20Prompt guardrails
- 27:03Model evaluation
- 28:07Production monitoring
- 29:08RAG future
- 30:09Q&A
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