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RAG is the ONLY way to make sure you models are keeping true to facts and source material. Fine-tuning a model before using RAG helps with shaping the style of
by binarymax 3y ago
RAG is the ONLY way to make sure you models are keeping true to facts and source material. Fine-tuning a model before using RAG helps with shaping the style of the summary, and gravitating towards more important facts presented.
- rolisz 3y agoIt still doesn't give guarantees, it just makes hallucinations much less likely.
- nostrebored 3y agoRAG doesn't give you this -- it gives you a higher probability that you're keeping true to facts and source material, but the model may still give you hallucinated responses.
- treprinum 3y agoThere are other methods than RAG over vector database; you can use basic TF-IDF or even full-text search to find candidate paragraphs and put them into the context.
- binarymax 3y agoOf course. “Retrieval” in RAG doesn’t require a special kind of retriever. As long as the relevance is tuned for the top documents to seed the prompt context, it doesn’t matter what kind of search backend you use.
- treprinum 3y agoAgreed though R in RAG typically means specifically vector search. Not sure how this was called before vector DBs were popularized by LLaMAIndex, probably just retrieval systems with LLMs.
- binarymax 3y agoThat's not true. Information Retrieval needs much more than vector search. I've contributed to a book on the subject: https://aipoweredsearch.com https://aipoweredsearch.com
- QuantumGood 3y agoAny more info? The book's not published yet, I see.
- natsucks 3y agonot to mention staying true to their benchmarks...