Deep Papers
RAG vs Fine-Tuning
Feb 8, 2024
This podcast explores the tradeoffs between RAG and fine-tuning for LLMs. It discusses implementing RAG in production, question and answer generation using JSON and LOM models, using GPT for test question generation in agriculture, evaluating relevance in email retrieval, and the use of RAG and fine-tuning for QA pair generation.
39:49
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Quick takeaways
- Rag and fine-tuning are two different approaches used in ML, with rag offering more advanced capabilities for search and retrieval.
- Implementing rag in production can be challenging due to various steps and problems involved in storing, retrieving, and evaluating knowledge base articles.
Deep dives
Comparison between search and retrieval methods and rag
Search and retrieval methods, which are the traditional ML version of rag, are commonly used in recommendation systems and search engines. Rag, on the other hand, adds an augmented generation aspect to search and retrieval, allowing for more advanced capabilities. Both search and retrieval methods and rag are used for similar purposes and share use cases.
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