🌐 RAG vs. Context-Window in GPT-4: accuracy, cost, & latency

The blog post discusses the efficiency of Retrieval Augmented Generation (RAG) over context-window-stuffing in improving the performance of large language models like GPT-4, particularly in terms of accuracy, cost, and latency. RAG, which is favored for creating hyper-specific responses, significantly reduces the cost to about 4% of GPT-4-Turbo's cost while maintaining high accuracy, especially for search-style queries. The analysis covers two RAG pipelines: Llama-Index and OpenAI's new assistant API's retrieval tool, with Llama-Index showing slightly lower costs. The post highlights that while RAG incurs a fixed reasoning cost, its latency remains competitive, especially when handling offline data. The discussion concludes that as the LLM ecosystem evolves, sophisticated RAG techniques are likely to become more prevalent, potentially overshadowing larger context windows, and emphasizes the promising future of open-source LLM technologies like Llama-Index.

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