Less is More: Why Use Retrieval Instead of Larger Context Windows

What this post added

This post provides experimental evidence and analysis supporting the efficacy of retrieval-augmented generation (RAG) over large context windows for LLMs. It details experiments using the Natural Questions (NQ) and QuALITY datasets to show that LLMs struggle with large, unfiltered contexts, leading to decreased accuracy and increased costs. The post highlights how retrieval systems, like Pinecone, can extract relevant information more efficiently, improving accuracy per token and reducing resource consumption. It contrasts the performance of LLMs with large contexts versus those augmented with retrieved segments, demonstrating significant cost savings and maintained accuracy.