| Lol more like, Perplexity has a terminal diagnosis. Doing RAG using like.. prompt hacking and text embeddings + vector store when you have no access to the underlying model, nor ability to fine tune the generation for RAG, will fail. It will fail in an epic fashion compared to doing RAG the right way. What do I mean by RAG the right way? The RAG term has been overloaded. There's RAG that's just kind of bolted onto the LLM after it's been fine tuned for instruction following, and then there's RAG where the document/fact retrieval is a part of the LLM itself that is differentiated and optimized. Almost everyone is doing the first "hacky" kind of RAG, but Meta published in 2020, the "correct" way to do RAG, where you include a neural retriever in the feedback loop. Almost no one is doing this because it's more expensive (requires fine tuning the model), but will produce much better results than doing "bolted-on" RAG. https://ai.meta.com/blog/retrieval-augmented-generation-stre... Perplexity does not have access to any GPT model weights. It's unlikely they'll be able to compete on quality. It's game over. |