> It is a proven solution that effectively addresses fundamental LLM challenges such as hallucinations and lacking domain-specific knowledge. mm. > While RAG

> It is a proven solution that effectively addresses fundamental LLM challenges such as hallucinations and lacking domain-specific knowledge. mm. > While RAG has proven beneficial in reducing LLM hallucinations, it does have limitations. mhm. > [good models] support 32k-token-long contexts, showcasing a substantial improvement in embedding capabilities. This enhancement in embedding unstructured data also elevates RAG’s understanding of long contexts. mmmmhmmm. So, let me get this straight. A model with a long context makes RAG significantly more effective because you can put more context into the input. ...but a model with a really massive context window won't be significantly better? ?? > Vector databases, one of the cutting-edge AI technologies, are a core component in the RAG pipeline. Opting for a more mature and advanced vector database, such as Milvus, > Conclusion: RAG Remains a Linchpin for the Sustained Success of AI Applications. Aha! So, this is a sales pitch. Right. No. You're wrong. RAG, as it currently exists, is a dead end technology. It exists because models don't have a large enough context window. If models get a significantly larger context window, it will become mostly irrelevant. Obviously, there always going to be some technical / compute limitations on the window size, and at some level, you'll always need to filter down to the relevant context to put into the input, so yes, technically the approach will always be around in some form. However, RAG in it's current form, where you have a tiny context window and you put little vector db located snippets in it, well.... lets just say, if I was a vendor for a vector database product, I'd also be worried and also be producing opinion pieces like this. An open model with a massive context would solve these problems trivially for most people, and make most 'vector db' products unnecessary for most uses.