Enhancing Language Models: RAG vs CAG Techniques Explained
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In this riveting exploration by IBM Technology, we delve into the world of augmented generation techniques to supercharge those brainy language models. Picture this: Retrieval Augmented Generation (RAG), where the model goes on a thrilling quest through an external knowledge base to beef up its brainpower with relevant documents. On the flip side, we have Cache Augmented Generation (CAG), a method that stuffs all the knowledge into the model's brain at once. It's like cramming the entire library into your head before a quiz - intense stuff.
RAG, with its offline and online phases, is all about precision - like a sharpshooter aiming for accuracy tied to the retriever's performance. Meanwhile, CAG is the brute force approach, loading up on all potential info and daring the model to sift through it all. It's a battle of wits - finesse versus sheer horsepower. When it comes to speed, RAG adds an extra step in the workflow, while CAG blazes through with a single forward pass. It's like comparing a leisurely Sunday drive to a full-throttle race around the track.
Scaling up, RAG flexes its muscles, capable of handling massive datasets with ease, fetching only the juiciest bits. On the other hand, CAG hits a roadblock with its limited context size, forcing it to squeeze all that knowledge into a tight space. And let's not forget about data freshness - RAG's nimble footwork allows for quick updates, while CAG might stumble when the ground shifts beneath its feet. It's a showdown of adaptability and raw power, with each method bringing its own unique flair to the table. So, whether you're team RAG or team CAG, the choice is yours - pick your weapon and charge into battle with those language models!
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Image copyright Youtube
Image copyright Youtube
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