Navigating AI Models: Fast Data, Slow Data, and Implementation Strategies

With so much rapid change happening around AI models, is there a framework for when to build models, train models, fine-tune models, use RAG or something else? Let’s explore…

SHOW: 852

SHOW TRANSCRIPT: The Cloudcast #852 Transcript

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SHOW NOTES:

• What is Retrieval Augmented Generation (RAG)?
• Fine-Tuning vs. RAG
• Predictive AI vs. Generative AI
• Andrew Ng (always worth a watch IMO) https://youtu.be/sal78ACtGTc?si=dCoKXb5o4KB2XSer
• Harrison Chase (LangChain) https://youtu.be/pBBe1pk8hf4?si=lAit69_pDeRKcBGq
• https://blog.langchain.dev/what-is-a-cognitive-architecture/
• https://thenewstack.io/lets-get-agentic-langchain-and-llamaindex-talk-ai-agents/
• https://www.youtube.com/watch?v=N-IYzyi86yY
• https://blog.langchain.dev/what-is-an-agent/
• https://www.microsoft.com/en-us/research/project/autogen/
• https://www.crewai.com/ 

HOW SHOULD WE DECIDE WHICH AI MODEL TO USE FOR OUR APPLICATION?

• Build, buy, use open source, augment with your own data - so many decisions
• How often do you update models with new data? 

HOW DO WE BRING OUR COMPANY DATA / KNOWLEDGE TO OUR AI MODELS?

• Large Static Models - Predictive AI
• LLMs + Slow-Changing Data - Align the Model
• LLMs + Fast-Changing Data - RAG
• LLMs + Agent-Learned Data - Agentic AI

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