Was diving deeper into local LLM tinkering and I wanted to understand: when would I want to fine tune vs. use context? What use cases would either cater to? Sorry if this is a bit basic, but still learning the ropes in terms of LLM tinkering and got confused between the two. An answer to this will really help me focus on learning the right thing. Thanks.
If it helps, I currently want my LLM to “understand” some essays/blog entries then spit back summaries/comparisons between them.
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iam looking to do something similar using RAG piplines might be usefull as far as i understand to give the model extra context about the sides you want to summarize.
https://agi-sphere.com/retrieval-augmented-generation-llama2/
maybe you already know all this but i am also new and just recently stumbled upon this :)
It depends on a lot of factors obviously, but in general I have found that fine tuning is best used to make the model respond in specific ways using the information it already possessed before the fine tuning, while context is best used to add knowledge that the model doesn’t have.