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Academic Style Lecture on Concepts Surrounding RAG in Generative AI
Manage episode 480585949 series 3610932
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Episode Notes: Search, Not Superintelligence: RAG's Role in Grounding Generative AI
Summary
I demystify RAG technology and challenge the AI hype cycle. I argue current AI is merely advanced search, not true intelligence, and explain how RAG grounds models in verified data to reduce hallucinations while highlighting its practical implementation challenges.
Key Points
- Generative AI is better described as "generative search" - pattern matching and prediction, not true intelligence
- RAG (Retrieval-Augmented Generation) grounds AI by constraining it to search within specific vector databases
- Vector databases function like collaborative filtering algorithms, finding similarity in multidimensional space
- RAG reduces hallucinations but requires extensive data curation - a significant challenge for implementation
- AWS Bedrock provides unified API access to multiple AI models and knowledge base solutions
- Quality control principles from Toyota Way and DevOps apply to AI implementation
- "Agents" are essentially scripts with constraints, not truly intelligent entities
Quote
"We don't have any form of intelligence, we just have a brute force tool that's not smart at all, but that is also very useful."
Resources
- AWS Bedrock: https://aws.amazon.com/bedrock/
- Vector Database Overview: https://ds500.paiml.com/subscribe.html
Next Steps
- Next week: Coding implementation of RAG technology
- Explore AWS knowledge base setup options
- Consider data curation requirements for your organization
#GenerativeAI #RAG #VectorDatabases #AIReality #CloudComputing #AWS #Bedrock #DataScience
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