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Large language models (LLMs)have taken the world by storm.
This is exactly where Retrieval Augmented Generation (RAG) frameworks come in.
They work by combining a pre-trained LLM with a retrieval component that is connected to readily accessible information.
The retrieval system finds relevant information in a knowledge library like adatabase.
Chatbots and other technologies that use natural language processing can massively benefit from RAG.
The best of both worlds
Using a RAG approach brings several benefits.
One of the most important is the ability to make large language models more agile.
be used for the initial training of the LLM.
In turn, RAG is essential to building useful generativeAI toolsin these industries.
As an example, considerelectronic health recordsand medical histories.
These contain sensitive information protected by privacy laws.
Limitations to note
While RAG is a powerful approach, its not a silver bullet.
Its effectiveness depends on the quality of the retrieval system and the data being used.
If the retrieval system fails to find accurate or relevant documents, the generated output can be incorrect.
Instead, RAG requires pre-indexed datasets or specific databases that must be regularly updated as that data evolves.
However, it is usually still much easier to update this additional database than to retrain the foundational LLM.
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