Shownotes Transcript
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…
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**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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