Pros of Rag as a hack:
– Provides an alternative approach to traditional finetuning methods – Can potentially yield good results without extensive training – Offers flexibility in leveraging data for LLM applications
Cons of Rag as a hack:
– May not always produce optimal or reliable results
– Requires careful evaluation and understanding to avoid potential pitfalls – Not as thoroughly studied or proven as traditional finetuning methods
Note: The information provided is based on the given subject and does not reflect personal opinion or endorsement.
Discover the captivating audio session, spanning 68 minutes, that explores the intricacies of evaluating RAG. Uncover the advantages of RAG over finetuning and delve into the fascinating transformation of LlamaIndex from a tree-index builder to the highly versatile framework for maximizing data utilization in LLM applications. Listen and gain valuable insights.