"Start with prompting, advance to RAG, then consider fine-tuning only when earlier approaches prove insufficient" - this ladder is the answer to 90% of "should I fine-tune" questions I hear.
The mistake I keep seeing: teams jump to fine-tuning because it feels more technical and impressive, not because prompting or RAG actually failed them. Fine-tuning a model that was never given proper context is optimizing the wrong layer entirely.
One thing worth adding to the framework: the hybrid approach works but evaluation complexity compounds fast. You need separate metrics for whether RAG retrieves the right context AND whether the fine-tuned model handles it correctly. Debug surface area doubles overnight.
So true on the evaluation complexity, good call! I would bring that in as a follow-up answer when asked. I wouldn't bring it in right away, since we usually only have 2-3 minutes to answer interview questions.
"Start with prompting, advance to RAG, then consider fine-tuning only when earlier approaches prove insufficient" - this ladder is the answer to 90% of "should I fine-tune" questions I hear.
The mistake I keep seeing: teams jump to fine-tuning because it feels more technical and impressive, not because prompting or RAG actually failed them. Fine-tuning a model that was never given proper context is optimizing the wrong layer entirely.
One thing worth adding to the framework: the hybrid approach works but evaluation complexity compounds fast. You need separate metrics for whether RAG retrieves the right context AND whether the fine-tuned model handles it correctly. Debug surface area doubles overnight.
So true on the evaluation complexity, good call! I would bring that in as a follow-up answer when asked. I wouldn't bring it in right away, since we usually only have 2-3 minutes to answer interview questions.