Comparing RAG, Long-Context Models, and Lightweight Alternatives

The following resources offer valuable insights into the evolving landscape of Retrieval-Augmented Generation and its alternatives. These articles were instrumental in shaping the ideas and approaches behind this Blueprint:

1. RAG vs. Long-Context Models: Do we still need RAG?

This article delves into the ongoing debate between RAG techniques and emerging long-context language models with expansive token windows, discussing the advantages and limitations of each approach.

2. Roaming RAG – RAG without the Vector Database

This piece introduces “Roaming RAG,” a streamlined alternative to traditional RAG that eliminates the need for vector databases, allowing language models to navigate well-structured documents directly to find answers.