When AI pessimists talk about the apocalypse and AI taking over, they often overlook that even the most advanced language models struggle with reasoning and drawing conclusions based on intricate connections. Another problem is the prohibitively high costs of training or fine-tuning (adapting to your data) large language models (LLMs).

Graph RAG (retrieval-augmented generation) approach addresses both issues and represents an upgrade to the original RAG technique we previously discussed. Let’s explore those graphs!

In today’s episode, we will cover:

Revisiting original RAG

Let’s briefly revisit the key concepts behind RAG. This approach allows using LLMs on previously unseen data without the need for fine-tuning. In the RAG setup, the data is stored in vector form within an external database. Using RAG, the LLM retrieves necessary information from it and bases its answer to the user query on retrieved facts.

RAG conserves resources by avoiding continuous fine-tuning as data updates, while also enabling easy modification of external databases for dynamic data control.

What are limitations of original RAG?