Improving RAG: Strategies
# The Twelve-Factor RAG
## Introduction
Modern AI applications increasingly rely on Retrieval-Augmented Generation (RAG) to provide accurate, reliable responses grounded in trusted data sources. The twelve-factor RAG methodology describes best practices for building production-grade RAG... See more
jason liux.comIf you're building a RAG system, embeddings search alone won't cut it.
https://t.co/a8MIUG8bFM
Go beyond vector search in this new blog by @jxnlco, where we deep dive into how truly useful RAG systems need a multi-layered approach to address limitations of vector search and provide real... See more
Avtharx.comI'm interested in a RAG strategy that involves storing, querying, and evaluating retrieved content quality across multiple methods with the aim to gradually increase confidence in the most effective RAG method for specific questions or contexts. Over time, it would reduce costs by selecting the most efficient approach once it meets a certain... See more
Yoheix.com
Multi-Head RAG (MRAG), aims to improve retrieval accuracy for complex queries requiring that require fetching multiple documents with substantially different contents.
Real-world use cases to demonstrate MRAG improves up to 20% in relevance over standard RAG baselines.
Paper - Multi-Head... See more
Newly published @GoogleAI Research on RAG - Two-step RAG Outperforms standard RAG š¤Æ
š Smaller specialist LM to generate draft texts that are then fed to a larger generalist LM to verify.
Original Problem š:
LLMs struggle with factual inaccuracies and... See more