RAG vs. Fine Tuning
IBM Technology. Tighter focus on the two techniques teams most often confuse. Goes deeper on data freshness, source attribution, and the inference-time speed argument for fine-tuning. Worth watching if you are specifically trying to argue against an unnecessary fine-tune project.
AI Expert note
Good for stakeholder conversations because it separates knowledge freshness from model behavior. Still validate current fine-tuning costs, supported models and data-governance constraints before committing.
What you should get from this
Explain when retrieval is the right fix and when fine-tuning may actually help.
Watch or know first
Basic RAG and fine-tuning vocabulary.
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