The Stack Overflow Podcast It’s RAG time for LLMs that need a source of truth
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Mar 1, 2024 
Roie Schwaber-Cohen, a Staff Developer Advocate at Pinecone, shares insights on retrieval augmented generation (RAG) and the power of vector databases for GenAI applications. He delves into how embeddings enhance LLM response accuracy and addresses the challenges of AI-generated content hallucinations. The conversation highlights the art of information chunking to optimize data relevance and discusses strategies for improving query results using metadata. Roie emphasizes a balanced approach to embedding content, aiming for both depth and coherence in AI interactions.
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Roie's AI Journey and Insights
- Roie Schwaber-Cohen shared his journey from traditional AI to generative AI at Pinecone.
- He highlighted how embeddings open new ways to think about data semantically.
LLMs and the Role of Retrieval
- LLMs always hallucinate; they cannot be sole sources of truth.
- Retrieval-augmented generation uses retrieval as a source of truth, guiding LLMs with relevant context.
Chunking for Better Retrieval
- Break your knowledge base documents into smaller semantically coherent chunks.
- Embed these smaller chunks to improve semantic relevance and LLM response accuracy.