
Despite the impressive capabilities of large language models (LLMs) like GPT-4, Retrieval-Augmented Generation (RAG) remains a relevant and valuable approach for natural language processing tasks. LLMs still face constraints in terms of computation, memory, and fine-tuning challenges, and they struggle with maintaining consistency and understanding complex relationships across lengthy interactions. RAG can help address these limitations by providing the necessary context and grounding to generate more coherent and accurate responses.


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Large language models (LLMs) have indeed made remarkable strides, breaking barriers in natural language understanding and generation.

Why RAG Still Matters: Beyond Token Limits in LLMs

retrieval-augmented-generation-rag-remains-relevant-despite-advances-in-large-language-models


Retrieval-Augmented Generation (RAG) Remains Relevant Despite Advances in Large Language Models
