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It's still RAG, just the R in RAG is not vector-based anymore, no?
by PhilippGille 2y ago
It's still RAG, just the R in RAG is not vector-based anymore, no?
- zbyforgotp 2y agoWhat you describe sounds like Agetic RAG https://zzbbyy.substack.com/p/agentic-rag https://zzbbyy.substack.com/p/agentic-rag
- janalsncm 2y ago> The traditional way to do RAG is to find information relevant to a query - and then incorporate it into the LLM prompt together with the question we want it to answer. Technically this is incorrect. The original RAG paper used a seq2seq generator (BART) and involved two methods: RAG sequence and RAG token. RAG sequence used the same fixed documents and appended them to the input query (note, this is different from a decoder-only model). RAG token generates each token based on a different document. I only nitpick this because if someone is going to invent new fancy-sounding variants of RAG they should at least get the basics right.
- zbyforgotp 2y agoTraditional does not equate original. The original technique was never used widely and cannot be called the traditional way.
- koliber 2y agoYou’re right. Many people ppl e take a mental shortcut and assume that RAG is a vector DB search. Any kind of retrieval is retrieval. You can do keyword search. You can do a PageRank like query. You can sort content by date and send the most recent items to the LLM. It’s all retrieval. That is the R on Retrieval Augmented Generation.