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Academic Style Lecture on Concepts Surrounding RAG in Generative AI
Manage episode 480585949 series 3610932
محتوای ارائه شده توسط Pragmatic AI Labs and Noah Gift. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Pragmatic AI Labs and Noah Gift یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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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