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Fine-tuning vs RAG (Practical AI #238)
In this episode we welcome back our good friend Demetrios from the MLOps Community to discuss fine-tuning vs. retrieval augmented generation. Along the way, we also chat about OpenAI Enterprise, results from the MLOps Community LLM survey, and the orchestration and evaluation of generative AI workloads.
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- Demetrios Brinkmann – Twitter
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(00:07) - Welcome to Practical AI
(00:43) - Practical AI & Friends
(02:01) - Look into MLOps community
(04:19) - Changes in the AI community
(07:34) - Finding the norm
(08:30) - Matching models & uses
(11:21) - Stages of debugging
(13:10) - Layer orchestration
(16:26) - Practical hot takes
(21:46) - Fine-tuning is more work
(24:13) - Retrieval augmented generation
(31:23) - MLOps survey
(38:09) - The next survey
(41:50) - Enterprise hypetrain
(42:39) - OpenAI & your data
(43:19) - AI vendor lock-in?
(47:44) - Now what do we do?
(48:58) - Hype in the AI life
(56:35) - Goodbye
(57:23) - Outro