RAG vs. Fine Tuning
The discussion highlights the advantages of RAG, particularly its auditability and ease of use for developers, compared to the complexities introduced by fine tuning. While fine tuning shows promise in optimizing models and improving user experience, it raises concerns around data access control and memorization. Both approaches may evolve to be complementary, but the balance between them remains an open question as fine tuning technology advances.In this clip
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Related Questions
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data, as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data, as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business?