Retrieval-augmented technology (RAG) is a method used to “floor” massive language fashions (LLMs) with particular knowledge sources, typically sources that weren’t included within the fashions’ unique coaching. RAG’s three steps are retrieval from a specified supply, augmentation of the immediate with the context retrieved from the supply, after which technology utilizing the mannequin and the augmented immediate.
At one level, RAG appeared like it could be the reply to every thing that’s flawed with LLMs. Whereas RAG might help, it isn’t a magical repair. As well as, RAG can introduce its personal points. Lastly, as LLMs get higher, including bigger context home windows and higher search integrations, RAG is turning into much less essential for a lot of use instances.
In the meantime, a number of new, improved sorts of RAG architectures have been launched. One instance combines RAG with a graph database. The mixture could make the outcomes extra correct and related, notably when relationships and semantic content material are essential. One other instance, agentic RAG, expands the sources obtainable to the LLM to incorporate instruments and features in addition to exterior data sources, equivalent to textual content databases.
