RAG Won’t Save Your Messy Data: The Brutal Truth About AI Reliability - The Enterprise AI Show

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Retrieval augmented generation LLM Roie Schwaber-Cohen RAG Reasoning

Episode notes

SUMMARY: The RAG (Retrieval Augmented Generation) pattern is one of the most frequently used to augment LLMs with context-specific information. Let’s explore RAG. GUEST: Roie Schwaber-Cohen , Head of Developer Relations at Pinecone SHOW: 1018 SHOW TRANSCRIPT: The Reasoning Show #1018 Transcript SHOW VIDEO: SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! SHOW NOTES: Topic 1 - Welcome to the show. Tell us a little bit about your background, and what you focus on these days at Pinecone Topic 2 - Let’s begin by talking about RAG systems. What are they? Why do companies choose to use them? What benefits do they provide in AI systems? Topic 3 - At a high level, RAG sounds straightforward - retrieve relevant context, generate an answer. But in practice, where does it break first as systems scale? Topic 4 - I’ve heard that RAG systems can return answers that are technically correct but fundamentally wrong. What’s a concrete example of that happening in production - and why does it slip past most teams? Topic 5 - In traditional systems, we assume there’s a single source of truth.

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