The increasing context window in large language models (LLMs) raises questions about the relevance of retrieval augmented generation (RAG). While RAG combines LLMs with external knowledge sources for more accurate responses, the longer context windows in LLMs can potentially lead to more accurate and contextually relevant answers without the need for RAG. However, RAG still persists due to its complex capabilities and ability to optimize performance and accuracy. Fine-tuning and long context windows have their own challenges and limitations compared to RAG.
Table of contents
How RAG WorksWhy Long Context Windows Might Be the End of RAGWhy RAG Will Stick AroundWhy Not Fine-TuningComparing RAG vs. Fine-Tuning or Long Context WindowsOptimizing RAG Systems With Vector DatabasesSort: