RAG in 2024: State of the Art and the Path Forward — Recap from GenAI Summit
Introduction
Retrieval-Augmented Generation (RAG) has emerged as a dominant approach in the field of natural language processing, particularly for enterprises looking to leverage large language models while incorporating their proprietary information.
In this blog post, we’ll explore the current state of RAG in 2024, based on insights from Professor Tengyu Ma’s recent talk at the GenAI Summit this weekend, and examine the potential future directions for this technology.
We’ll answer 3 main questions today:
- Is RAG still worth it when there’s long context models and fine-tuning options?
- What’s the current RAG landscape?
- What will the future look like?
The RAG Advantage
RAG has gained significant traction over competing approaches like fine-tuning and long context transformers. The primary reason for this is its ability to efficiently incorporate external knowledge without the need to retrain or significantly alter the base language model.
RAG vs. Fine-tuning and Long Context Transformers
- Long Context Transformers: While powerful, these models require reading the entire “library” of information for each query, resulting in high computational costs and potential loss of relevant information.
