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.

• 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

"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."

• AWS Bedrock: https://aws.amazon.com/bedrock/
• Vector Database Overview: https://ds500.paiml.com/subscribe.html

• 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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